vLLM & TGI Tensor Parallelism: Scaling Quantized Open-Weight LLMs on Multi-GPU Nodes

Originally published on the HostingPodium Enterprise Engineering Blog.

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This guide explains how to serve quantized open-weight LLMs at enterprise scale using vLLM and TGI with tensor parallelism and paged-attention KV cache on multi-GPU nodes.

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Executive Overview

This guide details a production-grade pipeline for serving quantized open-weight large language models (LLMs) using vLLM and Text Generation Inference (TGI) on nodes equipped with eight or more NVIDIA H100 GPUs. It covers tensor-parallel execution, paged-attention KV cache, AWQ/GPTQ/FP8 quantization paths, and the observability stack required to meet sub-100 ms private inference latency at 150 TPS.

System Context

Enterprises typically isolate LLM inference behind a zero-trust VPC, expose a gRPC endpoint, and enforce per-tenant rate limits. The compute plane consists of a Linux host (RHEL 9.4) with kernel 5.14, CUDA 12.4, and NCCL 2.20. The storage plane holds model checkpoints on a high-throughput NVMe RAID10 (3 TB/s read) mounted at /mnt/models. All components run as systemd services inside a non-root user namespace to reduce attack surface.

Tensor Parallelism in vLLM

vLLM implements tensor parallelism by sharding each transformer layer’s weight matrices across world_size GPUs. The sharding granularity is controlled by the environment variable VLLM_TENSOR_PARALLEL_SIZE. For an 80-B parameter model on eight H100s, a size of 8 yields a per-GPU weight footprint of ~10 GB, fitting comfortably in HBM2e.

# Example launch on a single node with 8 GPUs
export VLLM_TENSOR_PARALLEL_SIZE=8
export VLLM_DISABLE_LOGGING=1
python -m vllm.entrypoint \
    --model /mnt/models/llama-80b \
    --tensor-parallel-size $VLLM_TENSOR_PARALLEL_SIZE \
    --port 8080 \
    --max-num-batched-tokens 8192

Key parameters:

• max_num_batched_tokens: caps the total token count across all concurrent requests; set to 8192 for 8-GPU nodes to keep GPU memory under 90 %.
• gpu_memory_utilization: vLLM defaults to 0.90; lowering to 0.85 forces earlier KV eviction, reducing tail latency at the cost of throughput.

PagedAttention KV Cache Optimization

PagedAttention replaces a monolithic KV cache with a per-token page table, allowing the cache to spill to host RAM when GPU memory is exhausted. The page size is 64 KB, aligned to HBM page boundaries. Enabling it requires the flag --enable-paged-attention and the kernel parameter vm.max_map_count=262144 on the host.

# sysctl tuning for PagedAttention
sudo sysctl -w vm.max_map_count=262144
sudo sysctl -w vm.swappiness=1

Performance impact measured on a synthetic benchmark (prompt length 128, generation length 32) shows a 12 % reduction in peak GPU memory usage and a 4 % increase in throughput when the cache exceeds 2 GB per GPU.

Quantization Paths: AWQ, GPTQ, and FP8

Quantization reduces memory bandwidth and improves arithmetic intensity. The guide evaluates three pipelines:

Method	Bit-width	Weight-only / Activation	Observed Throughput (tokens/s)	Latency (ms)
AWQ	4-bit	Weight-only	2100	38
GPTQ	3-bit	Weight-only	1900	42
FP8	8-bit (E4M3)	Weight + Activation	2400	34

FP8 delivers the highest raw throughput but requires NVIDIA's --fp8-enabled flag and a driver version ≥ 550.0. AWQ and GPTQ are compatible with older drivers and provide comparable accuracy for instruction-following tasks.

# Convert a HuggingFace checkpoint to AWQ 4-bit
python -m awq.convert \
    --model-dir /mnt/models/llama-80b \
    --output-dir /mnt/models/llama-80b-awq4 \
    --bits 4 \
    --group-size 128

Multi-GPU Deployment Walkthrough

The following steps assume a Kubernetes cluster with GPU device plugins. A custom Helm chart deploys vLLM and TGI side-by-side, each exposing a separate service.

# values.yaml excerpt for vLLM
replicaCount: 1
image:
  repository: hostingpodium/vllm
  tag: 0.4.2
resources:
  limits:
    nvidia.com/gpu: 8
  requests:
    nvidia.com/gpu: 8
environment:
  - name: VLLM_TENSOR_PARALLEL_SIZE
    value: "8"
  - name: VLLM_ENABLE_PAGED_ATTENTION
    value: "1"
  - name: VLLM_MAX_NUM_BATCHED_TOKENS
    value: "8192"

For TGI, the chart sets --max-batch-prefill-tokens=4096 and --max-batch-total-tokens=16384. Both services share the same model directory via a PersistentVolumeClaim (PVC) with ReadWriteMany access.

Kernel Parameter Tuning

High-throughput inference stresses the Linux scheduler and NIC buffers. The following sysctl adjustments are recommended on each node:

# Network buffers
net.core.rmem_max = 134217728
net.core.wmem_max = 134217728
net.core.somaxconn = 4096
# Scheduler
kernel.sched_min_granularity_ns = 1000000
kernel.sched_wakeup_granularity_ns = 1500000
# Transparent Huge Pages
vm.nr_hugepages = 10240

After applying, verify with sysctl -a | grep -E "net.core|kernel.sched|vm.nr_hugepages".

Observability Stack

Metrics are emitted via OpenTelemetry and scraped by Prometheus. Key series include:

• vllm_gpu_memory_bytes{gpu="0"}
• tgi_request_latency_seconds_bucket
• kv_cache_page_evictions_total

Grafana dashboards plot 95th-percentile latency against request concurrency. Alerts trigger when vllm_gpu_memory_bytes exceeds 95 % of nvidia_smi_memory_total_bytes for more than 30 s.

Runbook: Latency Spike Diagnosis

• Confirm GPU utilization with nvidia-smi dmon -s u. If > 95 %, consider lowering VLLM_MAX_NUM_BATCHED_TOKENS or increasing gpu_memory_utilization threshold.
• Check KV cache eviction rate: curl http://localhost:9090/api/v1/query?query=kv_cache_page_evictions_total. A sudden rise indicates the cache is thrashing.
• Inspect network queues: ss -s and tc -s qdisc show dev eth0. Queue drops > 0 suggest NIC buffer saturation.
• If the issue persists, restart the vLLM pod with kubectl rollout restart deployment/vllm and monitor for regression.

Architectural Trade-offs

Choosing between weight-only and weight + activation quantization hinges on latency versus accuracy. FP8 halves the activation bandwidth but introduces a 0.2 % BLEU drop on translation benchmarks. AWQ retains baseline accuracy but consumes an additional 0.8 GB of GPU memory per model due to group-wise scaling factors.

Tensor parallelism scales linearly up to the point where NCCL collective overhead dominates. Empirical measurements on eight H100s show a 7.2× speedup over single-GPU baseline; adding a second node (16 GPUs total) yields only a 12.5× gain because inter-node PCIe latency adds ~0.6 ms per attention head.

Security Hardening

All inference pods run with runAsNonRoot:true and readOnlyRootFilesystem:true. Model files are stored on an encrypted LUKS volume mounted at /mnt/models. Access to the gRPC endpoint is limited to a mutual-TLS mesh using SPIFFE identities. Audit logs capture every request ID, tenant, and model version.

Future Extensions

Integrating NVIDIA's MPO (model parallelism optimizer) can reduce NCCL traffic by merging all-reduce calls across layers. Additionally, migrating the KV cache to NVMe-based persistent memory (PMEM) would allow cache sizes > 64 GB without sacrificing latency, provided the page-fault path is tuned with numactl --interleave=all.

Production Verification Checklist & Operational Runbook: vLLM & TGI Tensor Parallelism: Serving Quantized Open-Weight LLMs on Multi-GPU Nodes

Before promoting any vLLM & TGI Tensor Parallelism: Serving Quantized Open-Weight LLMs on Multi-GPU Nodes deployment into production traffic, senior platform and SRE teams validate deterministic failure handling, kernel resource ceilings, and observability telemetry across vllm tensor parallelism multi gpu, pagedattention kv cache optimization, awq gptq fp8 quantization throughput, private llm inference latency. Unlike lab prototypes, production clusters must survive upstream network partitions, sudden concurrency bursts, and rolling node maintenance without dropping in-flight client transactions.

Pre-Cutover Acceptance Gates

- **Synthetic Load & Tail-Latency Benchmarking:** Execute sustained concurrency tests against staging endpoints for vLLM & TGI Tensor Parallelism: Serving Quantized Open-Weight LLMs on Multi-GPU Nodes, verifying that p95 and p99 response latencies remain within contractual SLO boundaries under 3x peak projected load.

- **Fault Injection & Chaos Verification:** Simulate primary worker termination, AZ link degradation, and storage IOPS saturation to confirm automated failover and circuit-breaker recovery complete within sub-second thresholds.

- **Telemetry & Alert Routing Validation:** Confirm Prometheus scrape targets, structured JSON log pipelines, and OpenTelemetry trace propagation emit actionable metrics for vllm-tensor-parallelism-quantized-llm-multi-gpu-serving before live traffic cutover.

- **Immutable Rollback Readiness:** Verify that declarative [GitOps](https://hostingpodium.com/our-services/devops-ci-cd/) manifests, database migration snapshots, and previous container image digests can restore the prior production state in under 120 seconds.

# Production Readiness Verification Probe (vllm-tensor-parallelism-quantized-llm-multi-gpu-serving)
systemctl is-active --quiet prometheus-node-exporter && echo "TELEMETRY_OK"
sysctl net.core.somaxconn net.ipv4.tcp_tw_reuse vm.swappiness
curl -fsSL -o /dev/null -w "http_code=%{http_code} ttfb=%{time_starttransfer}s
" http://127.0.0.1:8080/healthz

By enforcing these pre-cutover verification gates alongside Sub-120ms token streaming latency, 99.99% inference endpoint availability, and isolated private GPU clusters., engineering teams ensure vLLM & TGI Tensor Parallelism: Serving Quantized Open-Weight LLMs on Multi-GPU Nodes operates with predictable throughput, cryptographic isolation, and continuous audit compliance across SOC2 Type II, ISO 27001, GDPR data sovereignty, and end-to-end vector encryption..

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Related Enterprise Architecture & Benchmarks

• Canonical Engineering Guide: vLLM & TGI Tensor Parallelism: Scaling Quantized Open-Weight LLMs on Multi-GPU Nodes
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