Can Llama 3.1 70B run on AMD Instinct MI350X 288GB?

YES — Runs Great

A79Great

Estimated from fit model

Llama 3.1 70B needs ~77.3 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~149 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) — 77.3 GB, 148.7 tok/s, Runs well

77.3 GB required288.0 GB available

27% VRAM used

Fit status

Runs well

Decode

148.7 tok/s

TTFT

1302 ms

Safe context

128K

Memory

77.3 GB / 288.0 GB

Memory breakdown

Weights42.7 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom28.8 GB

See how fast it feels

See how fast it feelsLlama 3.1 70B on AMD Instinct MI350X 288GB
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: 148.7 tok/s decode · 1.3s TTFT (warm) · 372 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	148.7 tok/s	710 ms	128K
Coding	A	Runs well	148.7 tok/s	1302 ms	128K
Agentic Coding	A	Runs well	148.7 tok/s	1893 ms	128K
Reasoning	A	Runs well	148.7 tok/s	1538 ms	128K
RAG	A	Runs well	148.7 tok/s	2367 ms	128K

Quantization options

How Llama 3.1 70B (70B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	B68
Q3_K_S	3	34.3 GB	Low	B69
NVFP4	4

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 MI350X 288GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 397B A17B	397B	S	78.9 tok/s
Devstral 2 123B Instruct	123B	S

Frequently asked questions

Can AMD Instinct MI350X 288GB run Llama 3.1 70B?

Yes, AMD Instinct MI350X 288GB can run Llama 3.1 70B with a A grade (Runs well). Expected decode speed: 148.7 tok/s.

How much VRAM does Llama 3.1 70B need?

Llama 3.1 70B (70B parameters) requires approximately 77.3 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 MI350X 288GB?

On AMD Instinct MI350X 288GB, Llama 3.1 70B achieves approximately 148.7 tokens per second decode speed with a time-to-first-token of 1302ms using Q4_K_M quantization.

Can AMD Instinct MI350X 288GB run Llama 3.1 70B for coding?

For coding workloads, Llama 3.1 70B on AMD Instinct MI350X 288GB receives a A grade with 148.7 tok/s and 128K context.

What context window can Llama 3.1 70B use on AMD Instinct MI350X 288GB?

On AMD Instinct MI350X 288GB, Llama 3.1 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 MI350X 288GB See all hardware for Llama 3.1 70B

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