Can Qwen3.5 122B A10B run on AMD Instinct MI250 128GB?

YES — Runs Great

C54Usable

○Estimated from fit model

Qwen3.5 122B A10B needs ~87.8 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q3_K_M quantization, expect ~34 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

F16 (Maximum quality) — 278.1 GB, exceeds 128.0 GB available

278.1 GB required128.0 GB available

217% VRAM needed

150.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

278.1 GB / 128.0 GB

Offload

50%

Memory breakdown

Weights250.1 GB

KV Cache14.3 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3.5 122B A10B on AMD Instinct MI250 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
 - Full privacy: your prompts never leave your machine
 - No per-token cost or rate limits
 - Works offline once the model is downloaded
 - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

 Model size Q4 (4-bit) Q8 (8-bit) FP16
 7B params ~4.3 GB ~7.5 GB ~14 GB
 13B params ~7.9 GB ~13.9 GB ~26 GB
 70B params ~42.7 GB ~74.9 GB ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 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	C	Runs well	33.9 tok/s	3119 ms	61K
Coding	C	Runs well	33.9 tok/s	5719 ms	61K
Agentic Coding	C	Runs well	33.9 tok/s	8319 ms	61K
Reasoning	C	Runs well	33.9 tok/s	6759 ms	61K
RAG	C	Runs well	33.9 tok/s	10398 ms	61K

Quantization options

How Qwen3.5 122B A10B (122B params) fits at each quantization level on AMD Instinct MI250 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	C45
Q3_K_S	3	59.8 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run Qwen3.5 122B A10B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "unsloth/Qwen3.5-122B-A10B-GGUF" \
 --hf-file "Qwen3.5-122B-A10B-GGUF-Q3_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options

Hardware that runs Qwen3.5 122B A10B well

👁 NVIDIA
NVIDIA H200 141GBBudget pick

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 85%.62.7 tok/s decode

Raises estimated decode speed by about 85%.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBBest value