Can DeepSeek R1 Distill Llama 8B run on Tesla P100 16GB?

YES — Runs Great

C53Usable

Estimated from fit model

DeepSeek R1 Distill Llama 8B 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 feelsDeepSeek R1 Distill Llama 8B on Tesla P100 16GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
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 DeepSeek R1 Distill Llama 8B (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 DeepSeek R1 Distill Llama 8B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server start

Frequently asked questions

Can Tesla P100 16GB run DeepSeek R1 Distill Llama 8B?

Yes, Tesla P100 16GB can run DeepSeek R1 Distill Llama 8B with a C grade (Runs well). Expected decode speed: 88.5 tok/s.

How much VRAM does DeepSeek R1 Distill Llama 8B need?

DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 8.6 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill Llama 8B?

The recommended quantization for DeepSeek R1 Distill Llama 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill Llama 8B run at on Tesla P100 16GB?

On Tesla P100 16GB, DeepSeek R1 Distill Llama 8B 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 DeepSeek R1 Distill Llama 8B for coding?

For coding workloads, DeepSeek R1 Distill Llama 8B on Tesla P100 16GB receives a C grade with 88.5 tok/s and 142K context.

What context window can DeepSeek R1 Distill Llama 8B use on Tesla P100 16GB?

On Tesla P100 16GB, DeepSeek R1 Distill Llama 8B 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 DeepSeek R1 Distill Llama 8B

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