Can Meta Llama 3.1 8B Instruct run on Tesla P100 16GB?

YES — Runs Great

C54Usable

Estimated from fit model

Meta Llama 3.1 8B Instruct needs ~8.6 GB VRAM. Tesla P100 16GB has 16.0 GB. With Q4_K_M quantization, expect ~89 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 8.6 GB, 88.5 tok/s, Runs well

8.6 GB required16.0 GB available

54% VRAM used

Fit status

Runs well

Decode

88.5 tok/s

TTFT

2188 ms

Safe context

142K

Memory

8.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsMeta Llama 3.1 8B Instruct on Tesla P100 16GB
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: 88.5 tok/s decode · 2.2s TTFT (warm) · 221 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	88.5 tok/s	1193 ms	142K
Coding	C	Runs well	88.5 tok/s	2188 ms	142K
Agentic Coding	C	Runs well	88.5 tok/s	3182 ms	142K
Reasoning	C	Runs well	88.5 tok/s	2585 ms	142K
RAG	C	Runs well	88.5 tok/s	3977 ms	142K

Quantization options

How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on Tesla P100 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C49
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C50
Q6_K	6	6.6 GB	High	C51
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

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

Frequently asked questions

Can Tesla P100 16GB run Meta Llama 3.1 8B Instruct?

Yes, Tesla P100 16GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 88.5 tok/s.

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

Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 8.6 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 Tesla P100 16GB?

On Tesla P100 16GB, Meta Llama 3.1 8B Instruct achieves approximately 88.5 tokens per second decode speed with a time-to-first-token of 2188ms using Q4_K_M quantization.

Can Tesla P100 16GB run Meta Llama 3.1 8B Instruct for coding?

For coding workloads, Meta Llama 3.1 8B Instruct on Tesla P100 16GB receives a C grade with 88.5 tok/s and 142K context.

What context window can Meta Llama 3.1 8B Instruct use on Tesla P100 16GB?

On Tesla P100 16GB, Meta Llama 3.1 8B Instruct can safely use up to 142K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Tesla P100 16GB See all hardware for Meta Llama 3.1 8B Instruct

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