⌘K
← All AI Models

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)

Parameters (total)
125.1B
Activated per token
10B (MoE)
Context window
262,144 tokens
Architecture source
HF config.json (verified)

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.

QuantizationBits / weightWeights onlyTotal + KV @4k ctxTotal + 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)

GPUVRAMBandwidthType
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.

GPUVRAMBandwidthType
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.

GPUVRAMBandwidthType
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 SiliconUnified memoryUsable 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.

DeviceMemoryBandwidthType
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.

SystemTypeMemoryUsable for AIBandwidthGPUs
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

Frequently asked questions