Serving Llama on GPU Infrastructure: Variants, Sizing, and Verification

Llama is the open-weight family most enterprises actually deploy, and serving it well is three decisions in sequence: which variant, at what precision, on what hardware — then a serving engine configured sanely and a verification gate before traffic. The figures circulate as fragments (70B at 40-something gigabytes quantized, 8B on a 24GB card); this page assembles them into the deployment path, with the arithmetic applied to Llama specifically and the quality duty that quantization creates made explicit.

Prerequisites: Pick the Variant and Precision Together

Variant and precision are one decision, not two: the small class (8B) serves on 24GB-class hardware at full precision, the large class (70B) needs 40-48GB quantized or multi-GPU at full precision, and the frontier class (400B) is a multi-GPU cluster decision — with quantization trading footprint for a quality delta you must validate on your tasks, and the license's commercial terms reviewed before either choice.

The figures follow the standard planning rules — roughly two gigabytes per billion parameters at FP16, a quarter of that at 4-bit — which hardware guides confirm against Llama specifically: 70B at 4-bit landing in the 40-48GB band, 8B comfortable on 24GB-class cards, and models past 40B routed to datacenter parts. Two duties attach to the table: production coverage reports quantization delivering major throughput and footprint gains, and your evaluation set decides how far down the precision ladder your tasks actually tolerate — quantize as far as the tasks allow, no further. And before either choice: Llama's community license carries commercial-use terms and a scale threshold; legal reviews the current version for your deployment size.

Size and Configure the Serving Stack

Apply the standard arithmetic to the chosen variant — weights at bytes-per-parameter plus KV cache at your context and concurrency, per the site's sizing method — then deploy a mainstream engine with continuous batching and paged attention as the defaults, adding quantization-aware settings where the precision choice demands them.

1. Size from the decision: the variant-and-precision choice fixes the weights term; your context length and target concurrency fix the KV cache term; their sum plus overhead selects the card count.
2. Configure the engine defaults: continuous batching and paged attention come standard in mainstream engines — treat them as the baseline, with KV cache tuning (the site's explainer covers the mechanics) as the next lever.
3. Set the quantization path: where the decision chose quantized serving, configure the engine's quantization support deliberately — the format choice interacts with batching and cache settings.
4. Plan capacity for the peak: concurrent sessions at your product's busy hour, not the average, set the fleet — the site's capacity pages carry the method.

Deployment tutorials for the family confirm the shape: the 8B class serving on 24GB-class GPUs with a mainstream engine, quantization plus batching plus cache tuning as the standard triad. For steady serving capacity once the deployment is proven, dedicated environments such as OneSource Cloud's private AI infrastructure are one committed option — the arithmetic above is what tells you how much capacity to commit.

Verify Before Traffic: Throughput, Latency, Quality

Three verifications gate traffic: throughput at your real concurrency (not the benchmark's), latency percentiles against your product's budget, and output quality on your tasks — the last mattering most after any quantization, because serving a degraded model efficiently is the failure mode nobody monitors.

• Throughput at concurrency: run your concurrency ladder — the site's benchmark-methodology page defines the manifest discipline — and read capacity at your latency SLO, not at peak throughput.
• Latency percentiles: first-token and per-token latency at the p95 your product promises, on the configured stack rather than the engine's defaults.
• Quality on your tasks: the evaluation set built for the precision decision runs again on the served artifact — same model, same quantization, same configuration — because a serving configuration can degrade what the precision choice preserved.

The quality gate leads for a reason: quantized deployments optimize enthusiastically, and an efficiently-served model that fails your tasks is worse than a slow one that passes. The gate runs on the served artifact, never a local copy, and its three records — the throughput curve, the latency percentiles, the quality scores — become the deployment's baseline for every later comparison.

FAQ

Which Llama variant should a team start with?

The small class unless a measured need says otherwise: 8B-class models serve on modest hardware, handle internal-assistant workloads well, and let the team build its serving and evaluation muscle — the 70B class earns its hardware when task quality measurably demands it, not before.

Is quantization worth the quality risk for serving?

Usually yes at 8-bit, conditionally at 4-bit: coverage reports major throughput and footprint gains, and moderate quantization typically holds quality on standard tasks — but the delta is task-specific, so the rule is quantize as far as your evaluation set says you can, and no further.

Can we use Llama commercially?

Under its community license, generally yes with obligations: the Llama license family carries acceptable-use terms and a scale threshold above which separate licensing applies — legal reviews the current terms for your deployment size before production, since license versions change with model releases.