TGI vs vLLM: which serves Llama 3 70B faster?

The debate over tgi vs vllm llama 3 70b speed usually starts with throughput charts but ends with operational reality. Both Hugging Face TGI and vLLM serve the 70B model well on H100 clusters, yet they diverge sharply in batching internals, memory accounting, and Day-2 ergonomics.

Capabilities

Batching and memory management

vLLM pioneered PagedAttention, mapping KV cache into non-contiguous blocks akin to virtual memory. This eliminates the fragmentation that naive continuous batching suffers when sequence lengths vary. TGI adopted its own form of continuous batching early, and recent releases added paged KV cache as well, but the scheduler still leans on a more monolithic token allocation per request.

For Llama 3 70B, a 128k context window turns KV cache into the dominant memory consumer. vLLM’s block table lets you pack 20–30% more concurrent sequences on the same 8×H100 node before OOM, assuming mixed short/long traffic. TGI’s implementation is tighter on pure throughput for fixed-size batches but less forgiving on tail latency when a 100k-token request lands next to a 2k one.

Quantization

Both support AWQ and GPTQ for 4-bit weights. TGI additionally ships FP8 inference paths validated on H100 transformers-engine, which can cut memory footprint further and raise tokens/sec if your GPUs support it. vLLM supports FP8 via its kernel registry but expects you to hand-roll the quant config. For Llama 3 70B, FP8 weight-only on TGI is a one-flag change:

docker run --gpus all -p 8080:80 ghcr.io/huggingface/text-generation-inference:latest \
  --model-id meta-llama/Meta-Llama-3-70B-Instruct \
  --quantize fp8

vLLM equivalent requires a quantization config file and --quantization fp8 plus environment flags for the kernel.

API and protocol

TGI exposes a Rust-based router with a dedicated /generate and OpenAI-compatible /v1/chat/completions. vLLM mirrors the OpenAI server surface almost exactly, making client swaps trivial. If you already standardized on OpenAI SDKs, both drop in:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8080/v1", api_key="EMPTY")
resp = client.chat.completions.create(
    model="meta-llama/Meta-Llama-3-70B-Instruct",
    messages=[{"role":"user","content":"Summarize TGI vs vLLM."}]
)

Parallelism primitives

TGI splits layers across GPUs using Rust-side NCCL wrappers; tensor parallel is the only first-class axis, with pipeline parallel available behind a flag. vLLM offers tensor, pipeline, and sequence parallelism via PyTorch primitives, which makes it easier to shard a 70B model across mixed node topologies. For a single 8-GPU box, either works; across InfiniBand-connected nodes, vLLM’s sequence parallel reduces all-reduce pressure.

Price and Cost Model

Self-hosting either framework on the same hardware yields identical raw GPU spend. The difference is utilization. Higher throughput per watt means fewer GPUs to meet a latency SLO. In a 70B serving scenario, vLLM’s memory efficiency often lets a single 8×H100 node replace what TGI would need nine or ten cards for under spiky load. That 10–15% reduction in provisioned capacity is the real cost story, not license fees—both are Apache 2.

If you’d rather not operate either stack, an OpenRouter-class gateway such as n4n.ai provides one OpenAI-compatible endpoint across 240+ models with automatic fallback when a provider is degraded, but you trade per-token margin for zero ops.

Cloud line items are identical: you pay for the instance, not the software. The hidden cost is engineering time. TGI’s single binary reduces the chance of a broken Python env at 3 a.m.; vLLM’s Pythonicity means your ML platform team can patch the scheduler without a Rust toolchain.

Latency and Throughput

Public MLPerf-style inference suites show vLLM winning mean tokens/sec by single-digit to low-double-digit percentages on mixed workloads at high concurrency. TGI claws back on small batch, fixed-sequence benchmarks because its CUDA graphs are aggressively tuned for static shapes. For Llama 3 70B interactive chat (TTFT < 500ms, 32 concurrent users), both deliver; vLLM holds p99 latency flatter as concurrency climbs.

Under long-context stress (e.g., 32k input, 1k output), TGI’s paged cache reduces eviction stalls, narrowing the gap. Neither will hit published FP16 theoretical bandwidth; expect real-world 70–85% of peak on H100 due to attention overhead. Time-per-output-token (TPOT) at batch 64 is typically 15–25ms for both; vLLM edges ahead when the batch mixes 1k and 50k context requests because its block allocator avoids reserving dead space.

Ergonomics

Deployment

TGI ships a single Docker image with sensible defaults and a health endpoint at /health. vLLM needs a Python env and explicit --tensor-parallel-size matching your GPU count:

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Meta-Llama-3-70B-Instruct \
  --tensor-parallel-size 8 \
  --max-model-len 131072

TGI auto-detects topology. vLLM’s CLI is more verbose but infinitely scriptable.

Observability

TGI emits Prometheus metrics at /metrics with label-rich counters for batch size, queue depth, and KV cache utilization. vLLM exposes similar via its /metrics route but requires you to parse PyTorch profiler dumps for kernel-level insight. TGI’s Rust frontend gives cleaner stack traces on OOM.

Config drift

vLLM reads everything from CLI or env; TGI supports a YAML manifest for model repos. For GitOps teams, TGI’s manifest is easier to review. Rolling restarts in TGI preserve warm KV caches for repeated prefixes; vLLM requires explicit --enable-prefix-caching to approach the same behavior.

Ecosystem

TGI is natively backed by Hugging Face hub: one model-id pulls weights, tokenizer, and generation config. vLLM relies on hf_hub too but expects you to manage revision pins. LangChain supports both via callback handlers. vLLM has stronger traction in research repos (e.g., LLM eval harnesses) because its Python-first design invites monkey-patching. TGI’s advantage is the Hub’s signed weight pipeline and built-in safety checks for Llama 3 guardrails.

Limits

• TGI requires a Rust build for custom kernels; extending the router is non-trivial.
• vLLM’s speculative decoding support for Llama 3 70B is experimental; TGI ships Medusa-style draft heads in beta.
• Both cap at the physical memory of your tensor-parallel group; 70B FP16 needs ~140GB weights + KV, so 8×H100 (640GB) is comfortable, 4×A100 80GB is tight with quantization.
• vLLM’s max model length default is 32k unless overridden; TGI defaults to the model’s declared context but will refuse to start if reserved KV exceeds VRAM.

Head-to-Head Comparison

Which to Choose

High-throughput batch jobs (offline summarization, eval sweeps): vLLM wins on tgi vs vllm llama 3 70b speed when you flood the server with thousands of independent sequences. The paged scheduler keeps GPU utilization high. Use vLLM with --max-num-seqs 256.

Latency-sensitive interactive serving with stable payloads: TGI’s tuned CUDA graphs and FP8 path give predictable p99. If your traffic is mostly 2–4k token chats, TGI’s simpler ops payoff.

Mixed long-context + short requests behind a gateway: vLLM’s block table handles variance better. Pair it with a routing layer that honors cache-control hints to reuse KV across users where possible.

You don’t want to run GPUs: Skip self-hosting. A gateway like n4n.ai fronts multiple providers and models behind one endpoint, metering per token and falling back automatically. That’s a different trade—margin for ops—but relevant if the tgi vs vllm llama 3 70b speed question is really “how do I ship a feature?”

Edge / constrained node: Neither fits a single 24GB card without 4-bit quant; both will work, but TGI’s single-binary story is easier to containerize at the edge.

Pick the framework that matches your load shape, not the benchmark that won last month.