Can Meta Llama 3.1 8B Instruct run on NVIDIA A100 40GB?

YES — Runs Great

C48Usable

Estimated from fit model

Meta Llama 3.1 8B Instruct needs ~11.0 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.0 GB, 112.0 tok/s, Runs well

11.0 GB required40.0 GB available

28% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

511K

Memory

11.0 GB / 40.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsMeta Llama 3.1 8B Instruct on NVIDIA A100 40GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	112.0 tok/s	943 ms	511K
Coding	C	Runs well	112.0 tok/s	1729 ms	511K
Agentic Coding	C	Runs well	112.0 tok/s	2514 ms	511K
Reasoning	C	Runs well	112.0 tok/s	2043 ms	511K
RAG	C	Runs well	112.0 tok/s	3143 ms	511K

Quantization options

How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C42
Q3_K_S	3	3.9 GB	Low	C42
NVFP4	4	4.5 GB	Medium	C43
Q4_K_M	4	4.9 GB	Medium	C43
Q5_K_M	5	5.8 GB	High	C43
Q6_K	6	6.6 GB	High	C43
Q8_0	8	8.6 GB	Very High	C44
F16Best for your GPU	16	16.4 GB	Maximum	C47

Get started

Copy-paste commands to run Meta Llama 3.1 8B Instruct on your machine.

Run

lms load hf-maziyarpanahi--meta-llama-3-1-8b-instruct-gguf && lms server start

升级选项

能流畅运行 Meta Llama 3.1 8B Instruct 的硬件

Mac Studio M2 Ultra 64GB经济之选

64 GB Unified (+24)

This setup is broadly balanced for this model.95.1 tok/s 解码

~$3,999 MSRP

Mac Studio M1 Ultra 64GB最佳性价比

64 GB Unified (+24)

This setup is broadly balanced for this model.90.2 tok/s 解码

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A100 40GB run Meta Llama 3.1 8B Instruct?

Yes, NVIDIA A100 40GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Meta Llama 3.1 8B Instruct need?

Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 11.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Meta Llama 3.1 8B Instruct?

The recommended quantization for Meta Llama 3.1 8B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Meta Llama 3.1 8B Instruct run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Meta Llama 3.1 8B Instruct achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run Meta Llama 3.1 8B Instruct for coding?

For coding workloads, Meta Llama 3.1 8B Instruct on NVIDIA A100 40GB receives a C grade with 112.0 tok/s and 511K context.

What context window can Meta Llama 3.1 8B Instruct use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, Meta Llama 3.1 8B Instruct can safely use up to 511K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A100 40GB See all hardware for Meta Llama 3.1 8B Instruct

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