Can Llama 3.1 70B run on AMD Instinct MI210 64GB?

YES — Tight Fit

A81Great

Estimated from fit model

Llama 3.1 70B needs ~54.9 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~28 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) — 54.9 GB, 28.4 tok/s, Tight fit

54.9 GB required64.0 GB available

86% VRAM used

Fit status

Tight fit

Decode

28.4 tok/s

TTFT

6825 ms

Safe context

46K

Memory

54.9 GB / 64.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsLlama 3.1 70B 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: 28.4 tok/s decode · 6.8s TTFT (warm) · 71 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	A	Runs well	28.4 tok/s	3723 ms	46K
Coding	A	Tight fit	28.4 tok/s	6825 ms	46K
Agentic Coding	A	Tight fit	28.4 tok/s	9927 ms	46K
Reasoning	A	Tight fit	28.4 tok/s	8066 ms	46K
RAG	A	Tight fit	28.4 tok/s	12408 ms	46K

Quantization options

How Llama 3.1 70B (70B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A77
Q3_K_S	3	34.3 GB	Low	A79
NVFP4	4	39.2 GB	Medium	A79
Q4_K_M	4	42.7 GB	Medium	A79
Q5_K_MBest for your GPU	5	50.4 GB	High	A79
Q6_K	6	57.4 GB	High	F0
Q8_0	8	74.9 GB	Very High	F0
F16	16	143.5 GB	Maximum	F0

Get started

Copy-paste commands to run Llama 3.1 70B on your machine.

Run

ollama run llama3.1

Your hardware

More models your AMD Instinct MI210 64GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 2.5 VL 72B	72B	S	27.6 tok/s
Qwen3-Coder-Next	80B	S	75.2 tok/s

Frequently asked questions

Can AMD Instinct MI210 64GB run Llama 3.1 70B?

Yes, AMD Instinct MI210 64GB can run Llama 3.1 70B with a A grade (Tight fit). Expected decode speed: 28.4 tok/s.

How much VRAM does Llama 3.1 70B need?

Llama 3.1 70B (70B parameters) requires approximately 54.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 70B?

The recommended quantization for Llama 3.1 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 70B run at on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, Llama 3.1 70B achieves approximately 28.4 tokens per second decode speed with a time-to-first-token of 6825ms using Q4_K_M quantization.

Can AMD Instinct MI210 64GB run Llama 3.1 70B for coding?

For coding workloads, Llama 3.1 70B on AMD Instinct MI210 64GB receives a A grade with 28.4 tok/s and 46K context.

What context window can Llama 3.1 70B use on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, Llama 3.1 70B can safely use up to 46K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI210 64GB See all hardware for Llama 3.1 70B

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<iframe src="https://willitrunai.com/embed/llama-3.1-70b-on-instinct-mi210-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

28.4 tok/s

3723 ms

46K

Coding

Tight fit

28.4 tok/s

6825 ms

46K

Agentic Coding

Tight fit

28.4 tok/s

9927 ms

46K

Reasoning

Tight fit

28.4 tok/s

8066 ms

46K

RAG

Tight fit

28.4 tok/s

12408 ms

46K

Quantization options

How Llama 3.1 70B (70B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A77
Q3_K_S	3	34.3 GB	Low	A79
NVFP4	4	39.2 GB	Medium	A79
Q4_K_M	4	42.7 GB	Medium	A79
Q5_K_MBest for your GPU	5	50.4 GB	High	A79
Q6_K	6	57.4 GB	High	F0
Q8_0	8	74.9 GB	Very High	F0
F16	16	143.5 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 2.5 VL 72B

72B

27.6 tok/s

Qwen3-Coder-Next

80B

75.2 tok/s

Frequently asked questions

Can AMD Instinct MI210 64GB run Llama 3.1 70B?

Yes, AMD Instinct MI210 64GB can run Llama 3.1 70B with a A grade (Tight fit). Expected decode speed: 28.4 tok/s.

How much VRAM does Llama 3.1 70B need?

Llama 3.1 70B (70B parameters) requires approximately 54.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 70B?

The recommended quantization for Llama 3.1 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.1 70B run at on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, Llama 3.1 70B achieves approximately 28.4 tokens per second decode speed with a time-to-first-token of 6825ms using Q4_K_M quantization.

Can AMD Instinct MI210 64GB run Llama 3.1 70B for coding?

For coding workloads, Llama 3.1 70B on AMD Instinct MI210 64GB receives a A grade with 28.4 tok/s and 46K context.

What context window can Llama 3.1 70B use on AMD Instinct MI210 64GB?

On AMD Instinct MI210 64GB, Llama 3.1 70B can safely use up to 46K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.