Can Llama 3.3 70B run on AMD Instinct MI250 128GB?

YES — Runs Great

A85Great

Estimated from fit model

Llama 3.3 70B needs ~61.3 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 61.3 GB, 55.4 tok/s, Runs well

61.3 GB required128.0 GB available

48% VRAM used

Fit status

Runs well

Decode

55.4 tok/s

TTFT

3493 ms

Safe context

128K

Memory

61.3 GB / 128.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B on AMD Instinct MI250 128GB
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: 55.4 tok/s decode · 3.5s TTFT (warm) · 139 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	55.4 tok/s	1905 ms	128K
Coding	A	Runs well	55.4 tok/s	3493 ms	128K
Agentic Coding	S	Runs well	55.4 tok/s	5081 ms	128K
Reasoning	A	Runs well	55.4 tok/s	4129 ms	128K
RAG	S	Runs well	51.0 tok/s	6907 ms	128K

Quantization options

How Llama 3.3 70B (70B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	A75
Q3_K_S	3	34.3 GB	Low	A76
NVFP4	4

Get started

Copy-paste commands to run Llama 3.3 70B on your machine.

Run

ollama run llama3.3

Your hardware

More models your AMD Instinct MI250 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	31.5 tok/s
Qwen 3.5 122B A10B	122B	S

Frequently asked questions

Can AMD Instinct MI250 128GB run Llama 3.3 70B?

Yes, AMD Instinct MI250 128GB can run Llama 3.3 70B with a A grade (Runs well). Expected decode speed: 55.4 tok/s.

How much VRAM does Llama 3.3 70B need?

Llama 3.3 70B (70B parameters) requires approximately 61.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B?

The recommended quantization for Llama 3.3 70B is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B run at on AMD Instinct MI250 128GB?

On AMD Instinct MI250 128GB, Llama 3.3 70B achieves approximately 55.4 tokens per second decode speed with a time-to-first-token of 3493ms using Q4_K_M quantization.

Can AMD Instinct MI250 128GB run Llama 3.3 70B for coding?

For coding workloads, Llama 3.3 70B on AMD Instinct MI250 128GB receives a A grade with 55.4 tok/s and 128K context.

What context window can Llama 3.3 70B use on AMD Instinct MI250 128GB?

On AMD Instinct MI250 128GB, Llama 3.3 70B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI250 128GB See all hardware for Llama 3.3 70B

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