Can you run Qwen3.5-122B-A10B locally?
Qwen3.5 family · Feb 2026 · 125.1B parameters (10B activated per token) · Hugging Face model card
Qwen3.5-122B-A10B is a mixture-of-experts model with 10B active parameters per token and 125.09B total open weights (official card: 122B total / 10B activated; HF safetensors.total=125,086,497,008). Native context is 262,144 tokens, apache-2.0. Local VRAM fit uses the 10B active count, not the 125B dense-equivalent: roughly 8 GB at Q4-class (calculated). Weights still occupy ~125B-class disk. Measured speeds: not yet published here.
Minimum: 8 GB+ memory (Q4-class on 10B active, calculated) · Recommended: 16 GB+ for comfortable context headroom (Q4-class on 10B active, calculated)
How much VRAM does Qwen3.5-122B-A10B need at each quantization?
Qwen3.5-122B-A10B needs 83.0 GB of VRAM at Q4_K_M. The table below lists weights-only size and total VRAM including overhead for each common quantization level with 4k and 32k token contexts.
| Quantization | Bits / weight | Weights only | Total + KV @4k ctx | Total + KV @32k ctx |
|---|---|---|---|---|
| Q4_K_M | 4.8 | 75.1 GB | 83.0 GB | 85.8 GB |
| Q5_K_M | 5.7 | 89.1 GB | 98.4 GB | 101.3 GB |
| Q6_K | 6.6 | 103.2 GB | 113.9 GB | 116.7 GB |
| Q8_0 | 8.5 | 132.9 GB | 146.6 GB | 149.4 GB |
| FP16 | 16 | 250.2 GB | 275.6 GB | 278.4 GB |
Method: weights = parameters × bits-per-weight (Q4_K_M ≈ 4.8, Q5_K_M ≈ 5.7, Q6_K ≈ 6.6, Q8_0 ≈ 8.5, FP16 = 16), plus 10% loading overhead, plus KV cache from the verified architecture config (48 layers, 2 KV heads, 256 head dim). Source: HF-verified 2026-09-21 via api (gated=false params=125.09B safetensors.total=125086497008 ctx=262144 license=apache-2.0 repo=Qwen/Qwen3.5-122B-A10B@dc4d348443bc740c68e2d77492492c11606384d5 layers=48 kv_heads=2 head_dim=256). MoE: num_experts=256 num_experts_per_tok=8 active_params_b=10.0 (vendor card; VRAM fit uses active, not dense-equivalent). VRAM figures are documented calculations, not measurements.
Which GPUs can run Qwen3.5-122B-A10B locally?
At Q4_K_M with a 4k context, Qwen3.5-122B-A10B needs 83.0 GB of VRAM. The lists below are computed live from our GPU database and grouped by how much headroom the card has. Disclosure: CompareAIHardware.com participates in the Amazon Associates program and earns from qualifying purchases through links on this page. Affiliate relationships do not influence our recommendations.
Runs Qwen3.5-122B-A10B comfortably (25%+ VRAM headroom)
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| H200 SXM | 141 GB | 4800 GB/s | Data Center GPU | |
| AMD Instinct MI300X | 192 GB | 5300 GB/s | Data Center GPU | |
| NVIDIA B200 | 192 GB | 8000 GB/s | Data Center GPU | |
| AMD Instinct MI325X | 256 GB | 6000 GB/s | Data Center GPU | |
| AMD Instinct MI355X | 288 GB | 8000 GB/s | Data Center GPU | |
| NVIDIA B300 (Blackwell Ultra) | 288 GB | 8000 GB/s | Data Center GPU |
Minimum GPUs that fit Qwen3.5-122B-A10B at Q4_K_M
These GPUs hold the model but leave little headroom — keep contexts short.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 1792 GB/s | Pro GPU | Check price |
Needs 2+ GPUs to run Qwen3.5-122B-A10B
One of these cards is too small on its own, but a pair (tensor or pipeline parallel, ~90% efficiency) covers the 83.0 GB requirement. See our multi-GPU guide for setup.
| GPU | VRAM | Bandwidth | Type | |
|---|---|---|---|---|
| AMD Radeon Pro W7900 | 48 GB ×2 | 864 GB/s | Pro GPU | Check price |
| NVIDIA RTX PRO 5000 Blackwell | 48 GB ×2 | 1344 GB/s | Pro GPU | |
| RTX 6000 Ada | 48 GB ×2 | 960 GB/s | Pro GPU | Check price |
| RTX A6000 | 48 GB ×2 | 768 GB/s | Pro GPU | Check price |
| H100 SXM | 80 GB ×2 | 3350 GB/s | Data Center GPU | |
| NVIDIA A100 80GB SXM | 80 GB ×2 | 2039 GB/s | Data Center GPU | |
| NVIDIA H100 PCIe 80GB | 80 GB ×2 | 2039 GB/s | Data Center GPU |
Can a Mac run Qwen3.5-122B-A10B?
Yes — these Apple Silicon machines fit Qwen3.5-122B-A10B at Q4_K_M, since macOS lets the GPU use about 75% of unified memory. Generation speed is bound by memory bandwidth, so the GB/s column matters as much as capacity.
| Apple Silicon | Unified memory | Usable by GPU (~75%) | Bandwidth |
|---|---|---|---|
| Apple M3 Max | 128 GB | 96 GB | 400 GB/s |
| M4 Max (MacBook Pro) | 128 GB | 96 GB | 546 GB/s |
| M3 Ultra (Mac Studio, 256 GB) | 256 GB | 192 GB | 819 GB/s |
Can Qwen3.5-122B-A10B run on mini PCs, Jetson, or NPU devices?
These edge and NPU devices from our database have enough memory for Qwen3.5-122B-A10B at Q4_K_M. Their memory bandwidth is far below discrete GPUs, so expect a fraction of desktop generation speed.
| Device | Memory | Bandwidth | Type |
|---|---|---|---|
| AMD Ryzen AI 9 HX 370 (Strix Point) | 96 GB | 120 GB/s | NPU Chip |
Which pre-built systems can run Qwen3.5-122B-A10B?
These mini PCs and workstations from our database fit Qwen3.5-122B-A10B at Q4_K_M. Usable-memory figures are conservative: Windows shares about half of system RAM with the GPU by default (Linux can expose more), macOS lets Apple Silicon GPUs use about 75% of unified memory, and Linux unified-memory systems such as GB10 expose roughly 90%. Generation speed is bound by memory bandwidth, so compare the GB/s column before buying.
| System | Type | Memory | Usable for AI | Bandwidth | GPUs |
|---|---|---|---|---|---|
| BOXX APEXX 8R (1x RTX PRO 6000 Blackwell) | Workstation | 96 GB | 96 GB | 1792 GB/s | 1 |
| GEEKOM A9 Mega AI Workstation | Workstation | 128 GB | 96 GB | — | 1 |
| NOVATECH RTX PRO 6000 AI Workstation | Workstation | 96 GB | 96 GB | — | 1 |
| System76 Thelio Major (1x RTX PRO 6000 Blackwell) | Workstation | 96 GB | 96 GB | 1792 GB/s | 1 |
| System76 Thelio Major (2x RTX 6000 Ada) | Workstation | 96 GB | 96 GB | 960 GB/s | 2 |
| Lenovo ThinkStation PGX | Workstation | 128 GB | 115.2 GB | 273 GB/s | 1 |
| NVIDIA DGX Spark | Workstation | 128 GB | 115.2 GB | 273 GB/s | 1 |
| BIZON G3000 G2 (4x RTX 5090) | Workstation | 128 GB | 128 GB | 1792 GB/s | 4 |
| Apple Mac Studio M2 Ultra (192GB) | Workstation | 192 GB | 144 GB | 800 GB/s | 1 |
| HP Z8 Fury G5 (4x RTX 6000 Ada) | Workstation | 192 GB | 192 GB | 960 GB/s | 4 |