What GPU do you need to run Qwen3.5-122B-A10B?
A new Framework Desktop ($6,799). No priced GPU card holds Qwen3.5-122B-A10B (about 79.1 GB at Q4); this system holds it with room for its full 256K context, at about 29 tok/s estimate. 6 of the 157 GPUs and systems we track fit it.
125.09B parameters, 10B active (mixture-of-experts) (Hugging Face, accessed 2026-10-18). Q4_K_M GGUF: 77.62 GB (source). Architecture config (Hugging Face config.json): 12 attention layers (the rest are recurrent) × 2 KV heads × 256 head dim, so about 24 KB of FP16 KV cache per token of context (how we estimate context).
Card: 122B total, 10B activated, 262K native context. Runs in stock llama.cpp (qwen35moe). Fit ignores the 0.91 GB vision projector. Release date is the Hugging Face repo creation date.
Other places to get it
Checked Nov 14, 2026. We list only copies we opened and checked. Check file hashes for copies from other uploaders.
- ModelScope: Lab's own account
- Ollama: Runs on your machine
ollama pull qwen3.5:122b-a10b
Single GPUs that fit, cheapest first
| Single GPU that fits | VRAM | Speed estimate | Max context estimate | Price |
|---|---|---|---|---|
| Framework Desktop, AMD Ryzen AI Max+ PRO 495 (Strix Halo), 192 GB | 192 GB | ~29 tok/s | 256K (full) | low $6,799 |
| NVIDIA DGX Spark (128GB) | 128 GB | ~33 tok/s | 256K (full) | low $6,949.99 |
| NVIDIA A100 80GB PCIe | 80 GB | ~129 tok/s | 54K · 103K (8-bit) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | 96 GB | ~124 tok/s | 256K (full) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | 96 GB | ~118 tok/s | 256K (full) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 96 GB | ~124 tok/s | 256K (full) | No price yet |
Every device that runs it
Graphics cards, Macs and mini PCs from the wider hardware list behind My rig, smallest usable memory first. Not sure about yours? Check your exact rig.
Show all 19 devices that run it
| Device | Type | Memory | Speed estimate | Max context estimate | Price |
|---|---|---|---|---|---|
| NVIDIA A100 80GB PCIe | Data center GPU | 80 GB | ~129 tok/s | 54K · 103K (8-bit) | No price yet |
| Apple M1 Ultra 128GB | Mac | 128 GB (~96 usable) | ~62 tok/s | 256K (full) | No price yet |
| Apple M2 Ultra 128GB | Mac | 128 GB (~96 usable) | ~62 tok/s | 256K (full) | No price yet |
| Apple M3 Max (40-core GPU) 128GB | Mac | 128 GB (~96 usable) | ~40 tok/s | 256K (full) | No price yet |
| Apple M4 Max (40-core GPU) 128GB | Mac | 128 GB (~96 usable) | ~49 tok/s | 256K (full) | No price yet |
| Apple M5 Max (40-core GPU) 128GB | Mac | 128 GB (~96 usable) | ~53 tok/s | 256K (full) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | Workstation GPU | 96 GB | ~124 tok/s | 256K (full) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | Data center GPU | 96 GB | ~118 tok/s | 256K (full) | No price yet |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | Workstation GPU | 96 GB | ~124 tok/s | 256K (full) | No price yet |
| AMD Ryzen AI Max 385 (128GB) | Mini PC | 128 GB | ~28 tok/s | 256K (full) | No price yet |
| AMD Ryzen AI Max 390 (128GB) | Mini PC | 128 GB | ~28 tok/s | 256K (full) | No price yet |
| AMD Ryzen AI Max+ 395 (128GB) | Mini PC | 128 GB | ~28 tok/s | 256K (full) | low $3,499.99 |
| NVIDIA DGX Spark (128GB) | Mini PC | 128 GB | ~33 tok/s | 256K (full) | low $6,949.99 |
| NVIDIA RTX Spark N1X laptop (6144-core GPU, 128GB) | Laptop | 128 GB | ~36 tok/s | 256K (full) | No price yet |
| Apple M2 Ultra 192GB | Mac | 192 GB (~144 usable) | ~62 tok/s | 256K (full) | No price yet |
| Apple M3 Ultra 256GB | Mac | 256 GB (~192 usable) | ~62 tok/s | 256K (full) | No price yet |
| Apple M5 Ultra 256GB | Mac | 256 GB (~192 usable) | ~75 tok/s | 256K (full) | No price yet |
| Framework Desktop, AMD Ryzen AI Max+ PRO 495 (Strix Halo), 192 GB | Mini PC | 192 GB | ~29 tok/s | 256K (full) | low $6,799 |
| Apple M5 Ultra 512GB | Mac | 512 GB (~384 usable) | ~75 tok/s | 256K (full) | No price yet |
Multi-GPU: cards that need two or more
| Cards | Total VRAM | Speed estimate | Max context estimate |
|---|---|---|---|
| 4 × AMD Radeon RX 7900 XTX | 96 GB | ~76 tok/s | 256K (full) |
| 4 × NVIDIA GeForce RTX 3090 | 96 GB | ~86 tok/s | 256K (full) |
| 4 × NVIDIA GeForce RTX 4090 | 96 GB | ~90 tok/s | 256K (full) |
| 3 × NVIDIA GeForce RTX 5090 | 96 GB | ~124 tok/s | 256K (full) |
| 2 × NVIDIA RTX A6000 | 96 GB | ~75 tok/s | 256K (full) |
Speed calculator
estimateEstimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works