willitrun·ai

Can Qwen3.5 122B A10B run on Radeon Pro W7900 48GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

Qwen3.5 122B A10B needs ~79.8 GB but Radeon Pro W7900 48GB only has 48.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: HighStack: StandardBottleneck: Memory capacity
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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) — 270.1 GB, exceeds 48.0 GB available
270.1 GB required48.0 GB available
563% VRAM needed

222.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

4K

Memory

270.1 GB / 48.0 GB

Offload

80%

Memory breakdown

Weights250.1 GB
KV Cache14.3 GB
Runtime0.9 GB
Headroom4.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen3.5 122B A10B on Radeon Pro W7900 48GB
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

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 79.8 GB, but this setup only exposes 48.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.5 tok/s42455 ms4K
CodingFToo heavy2.0 tok/s94840 ms4K
Agentic CodingFToo heavy2.0 tok/s140800 ms4K
ReasoningFToo heavy2.0 tok/s112083 ms4K
RAGFToo heavy2.0 tok/s176000 ms4K

Inference speed

Qwen3.5 122B A10B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3.5 122B A10B at Q3_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~9 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
MacBook Pro M4 Max 128GB
128 GBQ3_K_M9.3Offloads
Mac Studio M3 Ultra 256GB
256 GBQ3_K_M8.7Fits
Mac Studio M2 Ultra 128GB
128 GBQ3_K_M7.2Offloads
Mac Studio M1 Ultra 128GB
128 GBQ3_K_M6.8Offloads
2× RX 7900 XTX 24GB
48 GBQ3_K_M4.7Too big
MacBook Pro M4 Max 64GB
64 GBQ3_K_M4.5Too big
NVIDIARTX 5090 32GB
32 GBQ3_K_M2.8Too big
MacBook Pro M4 Pro 48GB
48 GBQ3_K_M2.6Too big
NVIDIA2× RTX 4090 24GB
48 GBQ3_K_M2.5Too big
NVIDIA2× RTX 3090 24GB
48 GBQ3_K_M2.3Too big
NVIDIARTX 4090 24GB
24 GBQ3_K_M2.0Too big
NVIDIARTX 4080 Super 16GB
16 GBQ3_K_M2.0Too big
NVIDIARTX 3090 24GB
24 GBQ3_K_M2.0Too big
NVIDIARTX 4070 12GB
12 GBQ3_K_M2.0Too big
NVIDIARTX 3060 12GB
12 GBQ3_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ3_K_M2.0Too big
RX 7900 XTX 24GB
24 GBQ3_K_M2.0Too big
MacBook Pro M3 Max 64GB
64 GBQ3_K_M2.0Too big
MacBook Pro M1 Max 64GB
64 GBQ3_K_M2.0Too big
NVIDIA4× RTX 3060 12GB
48 GBQ3_K_M2.0Too big

Estimates for single-stream decoding at Q3_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3.5 122B A10B (122B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
47.6 GB
LowF0
Q3_K_S
3
59.8 GB
LowF0
NVFP4
4
68.3 GB
MediumF0
Q4_K_M
4
74.4 GB
MediumF0
Q5_K_M
5
87.8 GB
HighF0
Q6_K
6
100.0 GB
HighF0
Q8_0
8
130.5 GB
Very HighF0
F16
16
250.1 GB
MaximumF0

Opciones de mejora

Hardware que ejecuta bien Qwen3.5 122B A10B

AMD Instinct MI350X 288GBOpción económica
288 GB VRAM (+240)8000 GB/s (+7136)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.90.9 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$8,000 MSRP

AMD Instinct MI300A 128GBMejor relación calidad-precio
128 GB VRAM (+80)5300 GB/s (+4436)
B
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.57.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$12,000 MSRP

AMD Instinct MI250X 128GBMejora AMD
128 GB VRAM (+80)3200 GB/s (+2336)
C
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.38.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$15,000 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run Qwen3.5 122B A10B?

No, Qwen3.5 122B A10B requires more memory than Radeon Pro W7900 48GB provides.

How much VRAM does Qwen3.5 122B A10B need?

Qwen3.5 122B A10B (122B parameters) requires approximately 79.8 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 Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Qwen3.5 122B A10B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 94840ms using Q3_K_M quantization.

Can Radeon Pro W7900 48GB run Qwen3.5 122B A10B for coding?

For coding workloads, Qwen3.5 122B A10B on Radeon Pro W7900 48GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Qwen3.5 122B A10B use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Qwen3.5 122B A10B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3.5 122B A10B feels slow on Radeon Pro W7900 48GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for Radeon Pro W7900 48GBSee all hardware for Qwen3.5 122B A10B
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Qwen3.5 122B A10B on Radeon Pro W7900 48GB? No — Alternativ…