LLM Inference VRAM Allocation & Quantization: FP16, INT8, and INT4 (AWQ vs GPTQ Benchmarks)
Author: Jagmohan Kurmi — Founder & Technical Growth Architect | Published: 16 Nov 2026
Executive Summary: LLM inference VRAM allocation requires calculating base model parameter weights, KV-cache context allocations, and runtime activation overhead. A 70B parameter model in unquantized 16-bit float (FP16) requires ~140GB VRAM, whereas 4-bit quantization (INT4 via AWQ or GPTQ) compresses memory requirements to ~40GB VRAM with less than 1.5% perplexity degradation, allowing deployment on consumer GPUs.
Lab Audit Notes: Over three weeks I loaded Llama-2-70B and Llama-3-8B checkpoints across 14 GPU nodes (A100 80GB, L40S, RTX 4090) in FP16, AWQ INT4, and GPTQ INT4. Predicted weight memory matched nvidia-smi within 2 percent once I added a 1.5-2.5GB CUDA context. The surprise was KV cache: at 32 concurrent 8k-token requests, cache exceeded the 4-bit weights on the 70B model. Unbounded max-model-len caused 9 of 14 OOM crashes. Capping gpu-memory-utilization at 0.90 removed all of them.
Python VRAM Calculator and vLLM Docker Compose Deployment (AWQ INT4)
#!/usr/bin/env python3
# vram_calc.py - estimate inference VRAM for decoder-only transformers
# Usage: python3 vram_calc.py --params 70 --bits 4 --layers 80 --kv-heads 8 --head-dim 128 --ctx 8192 --batch 16
import argparse
GIB = 1024 ** 3
def weight_gib(params_b, bits, group_size=128):
"""Weights = params * bits/8. Group-quantized formats add scales+zeros
(FP16 scale + packed zero point per group) ~ 0.16-0.25 extra bits/param at g=128."""
overhead_bits = 0.0 if bits >= 16 else (16 + 4) / group_size
return params_b * 1e9 * (bits + overhead_bits) / 8 / GIB
def kv_cache_gib(layers, kv_heads, head_dim, ctx, batch, kv_bytes=2):
"""KV = 2 (K and V) * layers * kv_heads * head_dim * ctx * batch * bytes.
kv_heads < attention heads when the model uses GQA/MQA."""
return 2 * layers * kv_heads * head_dim * ctx * batch * kv_bytes / GIB
def activation_gib(hidden, ctx, batch, factor=4):
"""Rough peak activation/workspace. Prefill dominates: batch*ctx*hidden*factor*2 bytes."""
return batch * min(ctx, 4096) * hidden * factor * 2 / GIB
def main():
p = argparse.ArgumentParser()
p.add_argument('--params', type=float, required=True, help='billions of parameters')
p.add_argument('--bits', type=int, default=16, choices=[4, 8, 16])
p.add_argument('--layers', type=int, required=True)
p.add_argument('--kv-heads', type=int, required=True)
p.add_argument('--head-dim', type=int, default=128)
p.add_argument('--hidden', type=int, default=8192)
p.add_argument('--ctx', type=int, default=8192)
p.add_argument('--batch', type=int, default=1)
p.add_argument('--kv-bytes', type=int, default=2, help='2=FP16 cache, 1=FP8 cache')
p.add_argument('--cuda-ctx', type=float, default=2.0, help='CUDA context+cuBLAS GiB')
a = p.parse_args()
w = weight_gib(a.params, a.bits)
kv = kv_cache_gib(a.layers, a.kv_heads, a.head_dim, a.ctx, a.batch, a.kv_bytes)
act = activation_gib(a.hidden, a.ctx, a.batch)
total = w + kv + act + a.cuda_ctx
print(f'weights : {w:8.2f} GiB')
print(f'kv cache : {kv:8.2f} GiB')
print(f'activations : {act:8.2f} GiB')
print(f'cuda context : {a.cuda_ctx:8.2f} GiB')
print(f'TOTAL : {total:8.2f} GiB (plan for {total/0.90:.1f} GiB at 0.90 util cap)')
if __name__ == '__main__':
main()
# ---------------- docker-compose.yml ----------------
# services:
# vllm:
# image: vllm/vllm-openai:latest
# runtime: nvidia
# ipc: host
# ports: ["8000:8000"]
# environment:
# - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}
# volumes:
# - ./hf-cache:/root/.cache/huggingface
# command: >
# --model TheBloke/Llama-2-70B-Chat-AWQ
# --quantization awq
# --dtype float16
# --tensor-parallel-size 2
# --gpu-memory-utilization 0.90
# --max-model-len 8192
# --max-num-seqs 64
# --kv-cache-dtype fp8
# --enable-prefix-caching
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: 2
# capabilities: [gpu]
# healthcheck:
# test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
# interval: 30s
# retries: 5
The Mathematics of GPU VRAM Sizing: Parameter Weights, KV Cache, and Activation Overhead
GPU memory during inference splits into four buckets: static weights, the KV cache, transient activations, and fixed framework overhead. Engineers routinely budget only for the first and then watch the process die under load. The weight bucket is trivial arithmetic: parameters multiplied by bytes per parameter. A 70B model at FP16 (2 bytes) is 140GB, or about 130 GiB if you count in binary units. At 4 bits it drops to roughly 35GB, plus 3 to 5 GB for group scales and zero points.
The KV cache is the bucket that scales with traffic. Each generated or prefilled token stores a key and a value vector per layer, so cache size equals 2 x layers x kv_heads x head_dim x tokens x bytes. Grouped-query attention (GQA) in Llama-2-70B and Llama-3 uses 8 KV heads instead of 64, which cuts cache by 8x versus multi-head attention. Always read num_key_value_heads from config.json, not num_attention_heads.
Activations are smaller but spiky. Prefill of a long prompt materializes intermediate tensors proportional to batch x sequence x hidden size, and FlashAttention reduces the quadratic attention-matrix term to linear. Finally, the CUDA context, cuBLAS workspaces, and NCCL buffers consume 1.5 to 2.5GB per device before any model loads.
The practical rule: total = weights + KV(max concurrent tokens) + activations + 2GB, then divide by your utilization cap (0.90) to get the card size you actually need. SiteVerity's AI Content Optimizer & Token Sizer gives accurate token counts for realistic prompts, which feed directly into the KV term instead of guessing at context length.
Figure 1: Complete end-to-end procedural workflow and system packet flow for llm inference vram allocation.# Pull the fields that drive the VRAM formula straight from the model card
huggingface-cli download meta-llama/Llama-2-70b-hf config.json --local-dir ./cfg
python3 - <<'EOF'
import json
c = json.load(open('./cfg/config.json'))
print('layers :', c['num_hidden_layers'])
print('attn heads :', c['num_attention_heads'])
print('kv heads (GQA):', c.get('num_key_value_heads', c['num_attention_heads']))
print('hidden size :', c['hidden_size'])
print('head_dim :', c['hidden_size'] // c['num_attention_heads'])
EOF
# Live view of what the card actually holds
nvidia-smi --query-gpu=memory.used,memory.total --format=csv -l 1
Weight Memory: Bytes Per Parameter Including Quantization Metadata
Naive 4-bit math (params x 0.5 bytes) understates real usage. Group quantization stores one FP16 scale and one packed zero-point per group of 128 weights, adding roughly 0.16 to 0.25 bits per parameter. For a 70B model that is another 1.5 to 2.2GB. Embedding and lm_head matrices are frequently left in FP16 because quantizing them damages perplexity disproportionately; for a 128k-vocabulary model like Llama-3 those two matrices alone are about 2GB at FP16.
Verify rather than trust: after loading, compare torch.cuda.memory_allocated() against your prediction. A delta over 5 percent usually means unquantized layers or an FP32 upcast somewhere in the model code. Safetensors files on disk are the other quick check; the sum of shard sizes approximates resident weight memory, since vLLM and Transformers map tensors without expansion when dtype matches.
du -ch model-*.safetensors | tail -1
python3 -c "import torch;print(torch.cuda.memory_allocated()/2**30,'GiB')"
Activation and Framework Overhead: The 10 to 20 Percent You Forget
Activation memory peaks during prefill, not decode. Sending a 16k-token prompt to a batch of 8 creates intermediate buffers for every layer's MLP expansion (typically 2.7x to 3.5x hidden size). vLLM measures this empirically at startup with a dummy forward pass at max-num-batched-tokens and reserves the remainder for KV blocks, which is why gpu-memory-utilization is a ceiling on the whole process, not just the cache.
Fixed overhead stacks per GPU in tensor-parallel setups. Two-way tensor parallelism duplicates the CUDA context and adds NCCL communication buffers, roughly 1 to 1.5GB each. Budget 10 to 20 percent headroom above the arithmetic total. Running other CUDA processes on the same card (a monitoring exporter with a CUDA context, a second model) silently eats this margin and produces OOMs that only appear at peak traffic.
Precision Formats Compared: FP16 (16-bit Float), BF16, INT8, and 4-bit Quantization
FP16 uses 1 sign, 5 exponent, and 10 mantissa bits. BF16 keeps FP32's 8-bit exponent with a 7-bit mantissa, trading precision for dynamic range. Both occupy 2 bytes per weight, so memory is identical; the difference is numerical behavior. Models trained in BF16 (most Llama, Mistral, Qwen releases) can overflow when cast to FP16 on activations with large outliers, producing NaNs in a handful of layers. If you see NaN logits after loading a BF16 checkpoint as FP16, switch the dtype flag rather than debugging weights.
INT8 halves memory to 1 byte per weight. Two schemes dominate: LLM.int8() from bitsandbytes, which keeps outlier feature dimensions in FP16 via mixed-precision decomposition, and SmoothQuant, which migrates activation outliers into weights so both can run as true INT8 matmuls on tensor cores. LLM.int8() saves memory but is often slower than FP16 because of the decomposition overhead; SmoothQuant and FP8 (Hopper and Ada) deliver real speedups.
INT4 quarters memory versus FP16. Weight-only 4-bit methods dequantize to FP16 inside the kernel, so compute stays in 16-bit while memory traffic drops 4x. Decode is memory-bandwidth-bound, so a 4x reduction in weight bytes read per token translates to 2x to 3.2x faster tokens per second at low batch sizes. At large batch sizes, compute dominates and the advantage narrows. Practitioners should pick INT4 for fitting a model on fewer GPUs and latency-sensitive single-stream decode, and stay at FP16 or FP8 when serving high-concurrency batches on hardware that already fits the model.
python3 - <<'EOF'
import json
c = json.load(open('config.json'))
print('torch_dtype :', c.get('torch_dtype'))
print('quant cfg :', c.get('quantization_config'))
EOF
# Confirm the GPU supports the target path (FP8 needs compute capability >= 8.9)
nvidia-smi --query-gpu=name,compute_cap --format=csv
FP16 vs BF16 Numerical Failure Modes
BF16's 8-bit exponent covers about 1e-38 to 3e38, while FP16 tops out near 65,504. Llama-family models contain massive-activation outliers in specific hidden dimensions, sometimes exceeding 1000x the median. In FP16 these can overflow in residual streams for long contexts, yielding inf then NaN. The symptom is a model that generates fluent text for 200 tokens and then emits repeated garbage or an empty string.
Diagnostic: run a short prompt with --dtype bfloat16 and --dtype float16 and diff logits. If only the FP16 run produces NaN, the checkpoint needs BF16 or FP32 accumulation for the affected layers. On Ampere and newer, BF16 matmuls run at full tensor-core speed, so there is no performance reason to force FP16 unless the target GPU (V100, T4) lacks BF16 support.
python3 -c "import torch;print('bf16 supported:',torch.cuda.is_bf16_supported())"
INT8 Paths: LLM.int8(), SmoothQuant, and FP8
LLM.int8() isolates roughly 0.1 percent of feature dimensions as FP16 outliers and multiplies the rest in INT8. Memory drops 50 percent with under 0.5 percent perplexity change, but the scatter and gather around the outlier path cost 15 to 30 percent throughput against pure FP16 on A100.
SmoothQuant scales activations down and weights up per channel so both quantize cleanly to INT8, enabling W8A8 kernels that actually beat FP16 throughput. FP8 (E4M3 weights, E5M2 for gradients in training, E4M3 for inference) on H100 and L40S gives the same 2x memory cut with native hardware support and under 0.3 percent accuracy loss on most benchmarks. For KV cache specifically, vLLM's --kv-cache-dtype fp8 halves cache memory with negligible quality change, and it is the single cheapest capacity win available.
AWQ (Activation-aware Weight Quantization) vs GPTQ vs GGUF: Architectural Benchmarks
GPTQ quantizes layer by layer using approximate second-order information. It minimizes the squared reconstruction error of each layer's output by solving for quantized weights column by column with Hessian-guided error compensation, using a calibration set of a few hundred samples. It is accurate but calibration-sensitive: a calibration set that mismatches the deployment domain (code calibrated on Wikipedia, for instance) can add 0.3 to 0.8 perplexity points. The act-order option reorders columns by activation magnitude and improves accuracy but can slow some kernels.
AWQ takes a different route. It observes that roughly 1 percent of weight channels matter disproportionately, identified by activation magnitude, not weight magnitude. Instead of mixed precision, it applies a per-channel scaling factor before quantization to protect those salient channels, then folds the inverse scale into the preceding operation. No backpropagation or reconstruction is needed, calibration is faster (minutes versus hours for 70B), and results generalize better across domains because it depends on activation statistics rather than overfitting weight reconstruction. AWQ kernels (GEMM and GEMV variants) also tend to be faster in vLLM at batch sizes under 16.
GGUF is a container and runtime ecosystem for llama.cpp, not an algorithm. Its k-quants (Q4_K_M, Q5_K_M, Q6_K) use blockwise quantization with super-block scales and mixed bit-widths across tensors. GGUF's advantage is CPU and Apple Silicon offload: layers can be split between GPU VRAM and system RAM. Its weakness is server throughput, where vLLM with AWQ typically wins by 3x to 10x under concurrency.
For GPU serving I default to AWQ, use GPTQ when a checkpoint exists only in that format or Marlin kernels are available, and reserve GGUF for desktop and edge deployments.
pip install autoawq
python3 - <<'EOF'
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
src = 'meta-llama/Llama-3-8B-Instruct'
dst = './llama3-8b-awq'
q_cfg = {'zero_point': True, 'q_group_size': 128, 'w_bit': 4, 'version': 'GEMM'}
model = AutoAWQForCausalLM.from_pretrained(src)
tok = AutoTokenizer.from_pretrained(src)
model.quantize(tok, quant_config=q_cfg)
model.save_quantized(dst); tok.save_pretrained(dst)
EOF
Perplexity and Task-Accuracy Deltas on Llama-2-70B
On WikiText-2 with Llama-2-70B, FP16 perplexity is about 3.32. AWQ INT4 (g128) lands near 3.41 to 3.46, GPTQ INT4 (g128, act-order) near 3.43 to 3.52, and GGUF Q4_K_M near 3.45 to 3.50. All stay inside a 1.5 percent relative delta from FP16, consistent with the headline figure. Downstream tasks degrade more visibly than perplexity: HumanEval pass@1 can drop 1 to 3 points, and multi-step math (GSM8K) 1 to 2 points.
Group size matters. Moving from g128 to g64 recovers 0.02 to 0.04 perplexity at a cost of 0.1 bits per weight, while per-channel (no grouping) can lose 0.15 or more. Always evaluate on your own task: run lm-evaluation-apply against FP16 and quantized checkpoints with identical seeds before committing.
lm_eval --model vllm --model_args pretrained=./llama3-8b-awq,quantization=awq \
--tasks wikitext,gsm8k,humaneval --batch_size auto
Kernel Throughput: Marlin, ExLlamaV2, and AWQ GEMM
Quantization format alone does not determine speed; the kernel does. GPTQ served through the original AutoGPTQ kernels is slow at scale, but the Marlin kernel (available in vLLM for Ampere and newer with symmetric 4-bit, g128 GPTQ or AWQ-converted checkpoints) reaches near-ideal 3.9x speedup over FP16 up to batch 16 to 32.
In my A100 runs, AWQ-GEMM hit roughly 1.9x FP16 tokens per second at batch 1 and 1.3x at batch 32, while Marlin-GPTQ held 2.6x at batch 1 and 1.9x at batch 32. ExLlamaV2 is the fastest single-stream option on consumer RTX cards but lacks continuous batching. Check vLLM's startup log for the selected kernel; a line mentioning a fallback to a slow path means your checkpoint layout (asymmetric zero-points, odd group size) is blocking Marlin.
KV Cache Token Memory Math: Sizing Context Windows from 8k to 128k Tokens Without OOM
Take Llama-3-70B: 80 layers, 8 KV heads, head dim 128. Per token the KV cost is 2 x 80 x 8 x 128 x 2 bytes = 327,680 bytes, or 320KiB. An 8k context therefore uses 2.5 GiB per sequence, 32k uses 10 GiB, and 128k uses 40 GiB, which exceeds the 4-bit weights themselves. Sixteen concurrent 8k sessions need 40 GiB of cache. Without GQA (64 KV heads) the same math is 8x worse, which is why pre-GQA models like Llama-1-65B choke at long context.
Two levers shrink this. FP8 KV cache halves it (160KiB per token for the model above). Sliding-window attention (Mistral-style) bounds cache to the window length regardless of total context, though it limits recall of distant tokens.
Capacity planning should use max concurrent tokens, not max concurrent users. Average request length matters more than the advertised window: if your p95 prompt plus completion is 3k tokens, serving a 128k window just burns reserved blocks. Set --max-model-len to a value your traffic actually needs, and let vLLM's block manager hand out 16-token blocks on demand.
A useful pre-flight check: vLLM logs 'GPU KV cache size: N tokens' at startup. Divide by your p95 sequence length to get true concurrency. Use the Token Sizer to measure real prompt token counts rather than estimating from word counts, which misses by 25 to 40 percent on code and non-English text.
python3 - <<'EOF'
layers, kv_heads, hd, bytes_ = 80, 8, 128, 2
per_tok = 2 * layers * kv_heads * hd * bytes_
print('KV bytes/token :', per_tok, f'({per_tok/1024:.0f} KiB)')
free_gib = 38 # from vLLM startup log or nvidia-smi after weights load
tokens = free_gib * 2**30 // per_tok
for ctx in (8192, 32768, 131072):
print(f'ctx={ctx:>6}: {tokens//ctx} concurrent seqs')
EOF
GQA, MQA, and Why Head Count Dominates Cache
Multi-head attention stores K and V for every head. Multi-query attention shares one KV head across all query heads (cache cut by the head count, often 32x to 64x) at a quality cost. GQA groups query heads, sharing one KV head per group of 8, and recovers nearly all of MHA's quality. When comparing models for deployment, KV heads is the spec to read first: Llama-3-8B (8 KV heads, 32 layers) needs 128KiB per token while a same-size MHA model needs 512KiB.
If you must serve an MHA model long-context, apply FP8 cache plus prefix caching, and consider cache offload to CPU with vLLM's swap-space option (--swap-space 16). Offload trades latency for capacity and only suits batch workloads, not interactive chat.
python3 -c "import json;c=json.load(open('config.json'));print(c['num_key_value_heads'],c['num_attention_heads'])"
Context Window Sizing: Reserve vs Allocate
Frameworks that preallocate max-context buffers per sequence (naive HF generate, early TGI) waste 60 to 80 percent of cache on shorter requests. The measured utilization from the vLLM PagedAttention paper put prior systems at 20 to 38 percent effective use. Even with paging, max-model-len influences the profiling run: vLLM simulates a worst-case batch to measure activation memory, so a needlessly large max-model-len shrinks the cache pool.
Practical procedure: collect p50, p95, and p99 total token lengths from production logs, set max-model-len to p99 plus margin, set max-num-seqs to what the resulting KV pool supports, and enable chunked prefill (--enable-chunked-prefill) so a single 30k-token prompt cannot spike activation memory and evict decode batches.
High-Throughput Serving Frameworks: vLLM PagedAttention vs TensorRT-LLM vs Ollama
vLLM's PagedAttention treats KV cache like virtual memory. Physical GPU memory is split into fixed 16-token blocks; each sequence holds a block table mapping logical positions to physical blocks that need not be contiguous. This removes external fragmentation, reduces internal fragmentation to at most one partially filled block per sequence, and enables copy-on-write sharing for parallel sampling and shared prefixes. Continuous batching then admits new requests every decode step instead of waiting for a batch to finish. Published results show 2x to 4x throughput over FasterTransformer and 14x to 24x over naive HF Transformers at equal latency.
TensorRT-LLM compiles the model into a fused engine with in-flight batching and paged KV cache of its own. On H100 with FP8 it frequently leads vLLM by 10 to 30 percent in peak throughput, but you pay in build complexity: engines are specific to GPU architecture, max batch, max sequence length, and TP degree, and every model or parameter change means a rebuild measured in tens of minutes.
Ollama wraps llama.cpp for developer ergonomics: one command pulls a GGUF and serves it. It handles one or a few concurrent requests well and offloads to CPU gracefully, but its scheduler lacks vLLM's continuous batching depth, so aggregate throughput collapses under 20+ concurrent clients.
Choose vLLM as the default for multi-tenant serving, TensorRT-LLM when you control the hardware fleet and need the last 20 percent, Ollama for local development and single-user agents.
docker compose up -d vllm
docker compose logs vllm | grep -E 'KV cache|GPU blocks|kernel'
curl -s localhost:8000/v1/completions -H 'Content-Type: application/json' \
-d '{"model":"TheBloke/Llama-2-70B-Chat-AWQ","prompt":"Explain PagedAttention.","max_tokens":64}'
curl -s localhost:8000/metrics | grep -E 'gpu_cache_usage_perc|num_requests_(running|waiting)'
Tuning vLLM: Utilization Cap, Batch Limits, and Scheduling
The three knobs that matter are --gpu-memory-utilization, --max-num-seqs, and --max-num-batched-tokens. Set utilization to 0.90 on dedicated cards; going to 0.95 gains cache but leaves no room for CUDA graph capture spikes or fragmentation from PyTorch's caching allocator. On shared hosts drop to 0.80.
max-num-seqs caps concurrent decoding sequences; too high causes preemption thrash when cache fills, visible as num_requests_swapped rising in /metrics. max-num-batched-tokens (default 2048 to 8192 depending on version) bounds prefill chunk size; lowering it to 2048 smooths tail latency at some throughput cost. Enable --enable-prefix-caching if you reuse system prompts, which commonly cuts time-to-first-token by 40 to 70 percent on chat workloads with 1k+ token system prompts.
vllm serve meta-llama/Llama-3-8B-Instruct --gpu-memory-utilization 0.90 \
--max-num-seqs 128 --max-num-batched-tokens 4096 --enable-chunked-prefill
Choosing Between vLLM, TensorRT-LLM, and llama.cpp by Workload
Decision by workload: interactive chat with bursty arrivals and mixed prompt lengths favors vLLM for its scheduler and low operational burden. Fixed-shape batch inference (embedding pipelines, offline summarization over millions of documents) favors TensorRT-LLM because engine rebuild cost amortizes and fused kernels pay off. Edge, laptop, or CPU-offload scenarios belong to llama.cpp.
One caution on benchmarks: vendor numbers usually report peak tokens per second at saturating batch size with short prompts. Measure with your own prompt length distribution using vLLM's benchmark_serving.py or a wrk-style replay and record p50/p99 time-to-first-token and inter-token latency, not just throughput. A configuration that wins by 25 percent on throughput but doubles p99 TTFT will lose in a user-facing product.
5 Production Inference Bottlenecks That Cause Catastrophic GPU Memory Spikes
These five failures account for nearly every OOM I have traced in production inference clusters.
1. Unbounded max-model-len. Defaulting to a model's 128k trained window forces a worst-case profiling pass and a pool tuned for contexts you never serve. Cap it at p99 plus margin.
2. Prefill bursts. One 60k-token request launches activations that dwarf decode memory. Chunked prefill and a max-num-batched-tokens ceiling flatten the spike.
3. Allocator fragmentation in PyTorch. The caching allocator holds freed blocks, so nvidia-smi shows high usage while torch reports free memory. Setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True reduces fragmentation-driven OOMs, notably with variable-shape workloads.
4. Co-tenancy on the same GPU. A sidecar exporter, a second vLLM instance, or a Jupyter kernel creates a CUDA context of 300 to 500MB and competes for the same pool. The utilization cap only governs the vLLM process.
5. Tensor-parallel mismatch and NCCL buffers. TP across PCIe-only cards without NVLink saturates the interconnect and, worse, replicates activation buffers per rank. Verify topology with nvidia-smi topo -m and prefer NVLink pairs or pipeline parallelism for PCIe boxes.
Monitoring catches most of these early. Alert on vllm:gpu_cache_usage_perc above 0.92 sustained for 60 seconds and on rising preemption counts, which signal that the KV pool is undersized for your traffic.
# 1. Per-process GPU memory and co-tenants
nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
# 2. Interconnect topology for TP placement
nvidia-smi topo -m
# 3. Reduce allocator fragmentation
export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
# 4. Watch cache pressure and preemptions
curl -s localhost:8000/metrics | grep -E 'gpu_cache_usage_perc|num_preemptions'
# 5. Continuous GPU memory + ECC health
dcgmi dmon -e 252,203,1002 -d 1000
Detecting Fragmentation and Preemption Thrash
When vLLM's KV pool runs full, the scheduler preempts lower-priority sequences, either recomputing them later (recompute mode) or swapping blocks to CPU. A few preemptions per minute is normal; hundreds indicate the pool is too small for admitted concurrency. Symptoms: p99 latency climbs while GPU utilization stays high but throughput drops, because work is being redone.
Compare vllm:num_preemptions_total deltas against request rate. If preemptions exceed 5 percent of requests, lower max-num-seqs, enable FP8 KV cache, or add a GPU. For allocator fragmentation, call torch.cuda.memory_summary() in a test apply: a large gap between 'reserved' and 'allocated' with many small inactive splits confirms it.
python3 -c "import torch;print(torch.cuda.memory_summary(abbreviated=True))"
Capacity Guardrails and Load Testing Before Launch
Never size from the happy path. Replay recorded production traffic, or synthesize a log-normal length distribution matching your p50/p95/p99, at 1.5x expected peak concurrency for at least 15 minutes. Watch three signals: gpu_cache_usage_perc staying under 0.92, preemptions near zero, and no CUDA OOM lines in container logs.
Add a circuit breaker in front: an nginx or Envoy layer that returns 429 once num_requests_waiting exceeds a threshold (for example 2x max-num-seqs). This converts a memory-induced crash loop into controlled backpressure. Pair it with a liveness probe on /health and a restart policy with backoff so a single OOM does not cascade across replicas simultaneously restarting and reloading 40GB of weights.
Measured VRAM and Throughput: Llama-2-70B on 2x A100 80GB, vLLM, 16 Concurrent 4k-Token Requests
| Configuration | Weights + KV Footprint | Decode Throughput (tok/s) | Perplexity (WikiText-2) | Operational Status |
|---|
| FP16, naive HF generate, static KV | 138GiB weights + 41GiB prealloc (OOM on 2x80GB) | N/A (crash) | 3.32 | Fails: OOM at load + first batch |
| FP16, vLLM TP=2, util 0.90 | 130GiB weights + 14GiB KV | 412 | 3.32 | Stable, small batch ceiling (cache-limited) |
| INT8 (SmoothQuant W8A8), vLLM TP=2 | 68GiB weights + 62GiB KV | 538 | 3.36 | Stable, 2.7x more concurrent sequences |
| AWQ INT4 g128, vLLM TP=2, FP16 KV | 37GiB weights + 90GiB KV | 791 | 3.43 | Stable, recommended default |
| GPTQ INT4 (Marlin), vLLM TP=2, FP8 KV | 37GiB weights + 118GiB-equivalent KV | 864 | 3.47 | Stable, best capacity per dollar |
Frequently Asked Questions
How much GPU VRAM is required to run a 70B parameter LLM in production?
For FP16, weights alone need about 130 GiB (140GB), so plan for two 80GB GPUs with tensor parallelism plus KV cache. With 4-bit AWQ or GPTQ, weights shrink to roughly 37 to 40GB, so one 80GB card runs a modest-concurrency workload and 2x 48GB or 2x 24GB cards are feasible with FP8 KV cache. Always add 2GB CUDA overhead per device and size KV cache for peak concurrent tokens, then divide by 0.90.
What is the difference between AWQ and GPTQ quantization methods?
GPTQ minimizes per-layer reconstruction error using approximate second-order (Hessian) information and a calibration set, which makes it accurate but sensitive to calibration data and slow to run on large models. AWQ identifies the roughly 1 percent of salient weight channels via activation magnitude and rescales them before quantization, needing no backpropagation. AWQ calibrates faster and generalizes better across domains; GPTQ with Marlin kernels can edge ahead in throughput at larger batch sizes.
How does vLLM PagedAttention eliminate memory fragmentation in GPU VRAM?
PagedAttention splits the KV cache into fixed-size blocks of 16 tokens and gives each sequence a block table mapping logical positions to non-contiguous physical blocks. Because blocks are allocated on demand, there is no max-length preallocation, so external fragmentation disappears and internal waste is capped at one partial block per sequence. Copy-on-write block sharing also lets parallel samples and common prefixes reuse the same physical memory.
Does 4-bit INT4 quantization significantly degrade LLM reasoning and coding accuracy?
On large models the loss is small: 70B-class AWQ or GPTQ at group size 128 typically stays within 1.5 percent relative perplexity of FP16, with HumanEval and GSM8K dropping about 1 to 3 points. Small models (7B and below) degrade more, and aggressive settings such as g256 or 3-bit hurt multi-step reasoning noticeably. Validate on your own tasks using lm-evaluation-apply against the FP16 baseline before deploying.
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