Can DeepSeek R1 Distill Qwen 14B run on NVIDIA A100 40GB?

YES — Runs Great

C50Usable

Estimated from fit model

DeepSeek R1 Distill Qwen 14B needs ~15.4 GB VRAM. NVIDIA A100 40GB has 40.0 GB. With Q4_K_M quantization, expect ~153 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) — 15.4 GB, 153.0 tok/s, Runs well

15.4 GB required40.0 GB available

39% VRAM used

Fit status

Runs well

Decode

153.0 tok/s

TTFT

1266 ms

Safe context

256K

Memory

15.4 GB / 40.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom4.0 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Qwen 14B on NVIDIA A100 40GB
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: 153.0 tok/s decode · 1.3s TTFT (warm) · 382 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	153.0 tok/s	690 ms	256K
Coding	C	Runs well	153.0 tok/s	1266 ms	256K
Agentic Coding	C	Runs well	153.0 tok/s	1841 ms	256K
Reasoning	C	Runs well	153.0 tok/s	1496 ms	256K
RAG	C	Runs well	153.0 tok/s	2301 ms	256K

Inference speed

DeepSeek R1 Distill Qwen 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek R1 Distill Qwen 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	140.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	89.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	80.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	76.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	75.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	65.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	54.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	51.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	33.2	Offloads
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	19.5	Offloads
RTX 4060 8GB	8 GB	Q4_K_M	7.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How DeepSeek R1 Distill Qwen 14B (14B params) fits at each quantization level on NVIDIA A100 40GB (40.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C43
Q3_K_S	3	6.9 GB	Low	C43
NVFP4	4	7.8 GB	Medium	C43
Q4_K_M	4	8.5 GB	Medium	C44
Q5_K_M	5	10.1 GB	High	C44
Q6_K	6	11.5 GB	High	C45
Q8_0	8	15.0 GB	Very High	C46
F16Best for your GPU	16	28.7 GB	Maximum	C48

Get started

Copy-paste commands to run DeepSeek R1 Distill Qwen 14B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-qwen-14b-gguf && lms server start

Frequently asked questions

Can NVIDIA A100 40GB run DeepSeek R1 Distill Qwen 14B?

Yes, NVIDIA A100 40GB can run DeepSeek R1 Distill Qwen 14B with a C grade (Runs well). Expected decode speed: 153.0 tok/s.

How much VRAM does DeepSeek R1 Distill Qwen 14B need?

DeepSeek R1 Distill Qwen 14B (14B parameters) requires approximately 15.4 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill Qwen 14B?

The recommended quantization for DeepSeek R1 Distill Qwen 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill Qwen 14B run at on NVIDIA A100 40GB?

On NVIDIA A100 40GB, DeepSeek R1 Distill Qwen 14B achieves approximately 153.0 tokens per second decode speed with a time-to-first-token of 1266ms using Q4_K_M quantization.

Can NVIDIA A100 40GB run DeepSeek R1 Distill Qwen 14B for coding?

For coding workloads, DeepSeek R1 Distill Qwen 14B on NVIDIA A100 40GB receives a C grade with 153.0 tok/s and 256K context.

What context window can DeepSeek R1 Distill Qwen 14B use on NVIDIA A100 40GB?

On NVIDIA A100 40GB, DeepSeek R1 Distill Qwen 14B can safely use up to 256K 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 DeepSeek R1 Distill Qwen 14B

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