Qwen3.5 122B A10B needs ~69.5 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q2_K quantization, expect ~5 tok/s.
Operating mode
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
208.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
272.0 GB / 64.0 GB
Offload
80%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 3.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~8.4 GB host RAM) | 4.0 tok/s | 26648 ms | 4K |
| Coding | F | Too heavy | 3.3 tok/s | 59242 ms | 4K |
| Agentic Coding | F | Too heavy | 2.3 tok/s | 121010 ms | 4K |
| Reasoning | F | Too heavy | 3.3 tok/s | 70013 ms | 4K |
| RAG | F | Too heavy | 2.3 tok/s | 151262 ms | 4K |
How Qwen3.5 122B A10B (122B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 47.6 GB | Low | C48 |
Q3_K_S | 3 | 59.8 GB | Low | F0 |
NVFP4 | 4 | 68.3 GB | Medium | F0 |
Q4_K_M | 4 | 74.4 GB | Medium | F0 |
Q5_K_M | 5 | 87.8 GB | High | F0 |
Q6_K | 6 | 100.0 GB | High | F0 |
Q8_0 | 8 | 130.5 GB | Very High | F0 |
F16 | 16 | 250.1 GB | Maximum | F0 |
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 99Upgrade-Optionen
RTX PRO 6000 Blackwell Workstation Edition 96GBGünstige WahlMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBBestes Preis-Leistungs-VerhältnisMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $9,999 MSRP
NVIDIA H20 96GBNVIDIA-UpgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
ca. $12,000 MSRP
Yes, NVIDIA A16 64GB can run Qwen3.5 122B A10B at Q2_K quantization (Very compromised (needs ~3.8 GB host RAM)). The recommended Q3_K_M requires 81.7 GB which exceeds available memory, but at Q2_K it needs only 69.5 GB. Expected decode speed: 5.3 tok/s.
Qwen3.5 122B A10B (122B parameters) requires approximately 81.7 GB at Q3_K_M quantization. On NVIDIA A16 64GB, it fits at Q2_K using 69.5 GB.
The recommended quantization is Q3_K_M, but on NVIDIA A16 64GB the best fitting quantization is Q2_K, which uses 69.5 GB.
On NVIDIA A16 64GB, Qwen3.5 122B A10B achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36700ms using Q2_K quantization.
For coding workloads, Qwen3.5 122B A10B on NVIDIA A16 64GB receives a F grade with 3.3 tok/s and 4K context.
On NVIDIA A16 64GB, Qwen3.5 122B A10B can safely use up to 10K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-122b-a10b-gguf-on-a16-64gb" 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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