Qwen3.5 35B A3B needs ~28.8 GB VRAM. RTX A5500 24GB has 24.0 GB. With Q4_K_M quantization, expect ~14 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
4.8 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~3.5 GB host RAM)
Decode
14.4 tok/s
TTFT
13456 ms
Safe context
4K
Memory
28.8 GB / 24.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 3.5 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 ~2.2 GB host RAM) | 16.8 tok/s | 6281 ms | 4K |
| Coding | D | Very compromised (needs ~3.5 GB host RAM) | 14.4 tok/s | 13456 ms | 4K |
| Agentic Coding | F | Too heavy | 10.9 tok/s | 25916 ms | 4K |
| Reasoning | D | Very compromised (needs ~3.5 GB host RAM) | 14.4 tok/s | 15903 ms | 4K |
| RAG | F | Too heavy | 10.9 tok/s | 32395 ms |
How Qwen3.5 35B A3B (35B params) fits at each quantization level on RTX A5500 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C50 |
Q3_K_SBest for your GPU | 3 | 17.2 GB | Low | C50 |
Copy-paste commands to run Qwen3.5 35B A3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 5090 32GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 153%.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 145%.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 50%.
~$4,000 MSRP
Yes, RTX A5500 24GB can run Qwen3.5 35B A3B with a D grade (Very compromised (needs ~3.5 GB host RAM)). Expected decode speed: 14.4 tok/s.
Qwen3.5 35B A3B (35B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
On RTX A5500 24GB, Qwen3.5 35B A3B achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13456ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 35B A3B on RTX A5500 24GB receives a D grade with 14.4 tok/s and 4K context.
On RTX A5500 24GB, Qwen3.5 35B A3B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-35b-a3b-gguf-on-rtx-a5500-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
| 4 |
19.6 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 21.3 GB | Medium | F0 |
Q5_K_M | 5 | 25.2 GB | High | F0 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
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.