Can DeepSeek LLM 67B run on AMD Instinct MI210 64GB?

YES — Tight Fit

B60Good

Estimated from fit model

DeepSeek LLM 67B needs ~54.0 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: StandardBottleneck: 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) — 54.0 GB, 29.6 tok/s, Tight fit

54.0 GB required64.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

29.6 tok/s

TTFT

6532 ms

Safe context

Memory

54.0 GB / 64.0 GB

Memory breakdown

Weights40.9 GB

KV Cache5.8 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsDeepSeek LLM 67B on AMD Instinct MI210 64GB
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: 29.6 tok/s decode · 6.5s TTFT (warm) · 74 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	B	Runs well	29.6 tok/s	3563 ms	4K
Coding	B	Tight fit	29.6 tok/s	6532 ms	4K
Agentic Coding	B	Tight fit	29.6 tok/s	9501 ms	4K
Reasoning	B	Tight fit	29.6 tok/s	7720 ms	4K
RAG	B	Tight fit	29.6 tok/s	11877 ms	4K

Inference speed

DeepSeek LLM 67B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DeepSeek LLM 67B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~20 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	19.5	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	16.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	14.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	12.4	Heavy offload
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	12.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	11.7	Fits
2× RTX 4090 24GB	48 GB	Q4_K_M	10.3	Heavy offload
2× RTX 3090 24GB	48 GB	Q4_K_M	9.4	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	8.3	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	6.2	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	5.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	5.0	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	4.5	Heavy offload
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.9	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	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 LLM 67B (67B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.1 GB	Low	B55
Q3_K_S	3	32.8 GB	Low	B57
NVFP4	4	37.5 GB	Medium	B58
Q4_K_M	4	40.9 GB	Medium	B58
Q5_K_MBest for your GPU	5	48.2 GB	High	B58
Q6_K	6	54.9 GB	High	F0
Q8_0	8	71.7 GB	Very High	F0
F16	16	137.4 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek LLM 67B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "deepseek-ai/deepseek-llm-67b-chat" \
  --hf-file "deepseek-llm-67b-chat-Q4_K_M.gguf" \
  -c 4096 -ngl 99

アップグレードオプション

DeepSeek LLM 67Bを快適に動かすハードウェア

AMD Instinct MI300A 128GBコスパ重視

128 GB VRAM (+64)5300 GB/s (+3662)

Raises estimated decode speed by about 233%.98.7 tok/sデコード

Raises estimated decode speed by about 233%.

Adds memory headroom for longer context windows and future model growth.

〜$12,000 MSRP

AMD Instinct MI250X 128GBコスパ最良

128 GB VRAM (+64)3200 GB/s (+1562)

Raises estimated decode speed by about 124%.66.4 tok/sデコード

Raises estimated decode speed by about 124%.

Adds memory headroom for longer context windows and future model growth.

〜$15,000 MSRP

AMD Instinct MI250 128GBAMDアップグレード

128 GB VRAM (+64)3200 GB/s (+1562)

Raises estimated decode speed by about 96%.57.9 tok/sデコード

Raises estimated decode speed by about 96%.

Adds memory headroom for longer context windows and future model growth.

〜$19,000 MSRP

Frequently asked questions

Can AMD Instinct MI210 64GB run DeepSeek LLM 67B?

Yes, AMD Instinct MI210 64GB can run DeepSeek LLM 67B with a B grade (Tight fit). Expected decode speed: 29.6 tok/s.

How much VRAM does DeepSeek LLM 67B need?

DeepSeek LLM 67B (67B parameters) requires approximately 54.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek LLM 67B?

The recommended quantization for DeepSeek LLM 67B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek LLM 67B run at on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, DeepSeek LLM 67B achieves approximately 29.6 tokens per second decode speed with a time-to-first-token of 6532ms using Q4_K_M quantization.

Can AMD Instinct MI210 64GB run DeepSeek LLM 67B for coding?

For coding workloads, DeepSeek LLM 67B on AMD Instinct MI210 64GB receives a B grade with 29.6 tok/s and 4K context.

What context window can DeepSeek LLM 67B use on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, DeepSeek LLM 67B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI210 64GB See all hardware for DeepSeek LLM 67B

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