Qwen3.5 122B A10B needs ~77.7 GB but RTX 4090 24GB only has 24.0 GB. Try a smaller quantization or lighter model.
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
244.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
268.0 GB / 24.0 GB
Offload
90%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 77.7 GB, but this setup only exposes 24.0 GB of usable VRAM.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How Qwen3.5 122B A10B (122B params) fits at each quantization level on RTX 4090 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 47.6 GB | Low | F0 |
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 |
Upgrade-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
Makes 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
NVIDIA DGX Spark 128GBNVIDIA-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.
No, Qwen3.5 122B A10B requires more memory than RTX 4090 24GB provides.
Qwen3.5 122B A10B (122B parameters) requires approximately 77.7 GB of memory with Q3_K_M quantization.
The recommended quantization for Qwen3.5 122B A10B is Q3_K_M, which balances quality and memory efficiency.
On RTX 4090 24GB, Qwen3.5 122B A10B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q3_K_M quantization.
For coding workloads, Qwen3.5 122B A10B on RTX 4090 24GB receives a F grade with 2.0 tok/s and 4K context.
On RTX 4090 24GB, 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.
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.
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-rtx-4090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
