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

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

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

Upgrade options

Hardware that runs DeepSeek LLM 67B well

AMD Instinct MI300A 128GBBudget pick

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

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

Raises estimated decode speed by about 233%.

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

~$12,000 MSRP

AMD Instinct MI250X 128GBBest value

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

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

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 upgrade

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

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

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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