NVIDIA H200 141GBOpción económica141 GB VRAM (+13)4800 GB/s (+1600)
C
Sube la velocidad estimada de decodificación alrededor de un 85%.62.7 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 85%.
~$30,000 MSRP
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.
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
4K
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 lowerSee 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 | 68.3 GB | Medium | C48 |
Q4_K_M | 4 | 74.4 GB | Medium | C48 |
Q5_K_M | 5 | 87.8 GB | High | C48 |
Q6_KBest for your GPU | 6 | 100.0 GB | High | C48 |
Q8_0 | 8 | 130.5 GB | Very High | F0 |
F16 | 16 | 250.1 GB | Maximum | F0 |
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 99Opciones de mejora
Hardware que ejecuta bien Qwen3.5 122B A10B
Frequently asked questions
Can AMD Instinct MI250 128GB run Qwen3.5 122B A10B?
Yes, AMD Instinct MI250 128GB can run Qwen3.5 122B A10B with a C grade (Runs well). Expected decode speed: 33.9 tok/s.
How much VRAM does Qwen3.5 122B A10B need?
Qwen3.5 122B A10B (122B parameters) requires approximately 87.8 GB of memory with Q3_K_M quantization.
What is the best quantization for Qwen3.5 122B A10B?
The recommended quantization for Qwen3.5 122B A10B is Q3_K_M, which balances quality and memory efficiency.
What speed will Qwen3.5 122B A10B run at on AMD Instinct MI250 128GB?
On AMD Instinct MI250 128GB, Qwen3.5 122B A10B achieves approximately 33.9 tokens per second decode speed with a time-to-first-token of 5719ms using Q3_K_M quantization.
Can AMD Instinct MI250 128GB run Qwen3.5 122B A10B for coding?
For coding workloads, Qwen3.5 122B A10B on AMD Instinct MI250 128GB receives a C grade with 33.9 tok/s and 61K context.
What context window can Qwen3.5 122B A10B use on AMD Instinct MI250 128GB?
On AMD Instinct MI250 128GB, Qwen3.5 122B A10B can safely use up to 61K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-122b-a10b-gguf-on-instinct-mi250-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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