48 GB VRAM (+16)768 GB/s (+192)
C
Raises estimated decode speed by about 26%.27.3 tok/s decode
Raises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
~$4,650 MSRP
Can Qwen3.5 35B A3B run on RTX 5000 Ada 32GB?
YES — Tight Fit
C49Usable
○Estimated from fit model
Qwen3.5 35B A3B needs ~29.9 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~22 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 29.9 GB, 21.6 tok/s, Tight fit
29.9 GB required32.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
21.6 tok/s
TTFT
8970 ms
Safe context
24K
Memory
29.9 GB / 32.0 GB
Memory breakdown
Weights21.3 GB
KV Cache4.1 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsQwen3.5 35B A3B on RTX 5000 Ada 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 21.6 tok/s decode · 9.0s TTFT (warm) · 54 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 21.6 tok/s | 4893 ms | 24K |
| Coding | C | Tight fit | 21.6 tok/s | 8970 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~1.2 GB host RAM) | 14.3 tok/s | 19707 ms | 24K |
| Reasoning | C | Tight fit | 21.6 tok/s | 10601 ms | 24K |
| RAG | D | Runs with offload (needs ~1.2 GB host RAM) | 14.3 tok/s | 24634 ms |
Quantization options
How Qwen3.5 35B A3B (35B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C48 |
Q3_K_S | 3 | 17.2 GB | Low | C50 |
NVFP4 | 4 |
Get started
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 "lmstudio-community/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Hardware that runs Qwen3.5 35B A3B well
48 GB VRAM (+16)1344 GB/s (+768)
C
Raises estimated decode speed by about 145%.52.9 tok/s decode
Raises estimated decode speed by about 145%.
Adds memory headroom for longer context windows and future model growth.
~$4,999 MSRP
48 GB VRAM (+16)864 GB/s (+288)
C
Raises estimated decode speed by about 46%.31.6 tok/s decode
Raises estimated decode speed by about 46%.
Adds memory headroom for longer context windows and future model growth.
~$5,500 MSRP