32 GB VRAM (+8)1792 GB/s (+1360)
B
Raises estimated decode speed by about 252%.72.9 tok/s decode
Raises estimated decode speed by about 252%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can Qwen3.5 27B run on RTX 4500 Ada 24GB?
YES — With Offload
C49Usable
○Estimated from fit model
Qwen3.5 27B needs ~23.2 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: LowStack: 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) — 23.2 GB, 20.7 tok/s, Runs with offload
23.2 GB required24.0 GB available
97% VRAM used
Fit status
Runs with offload
Decode
20.7 tok/s
TTFT
9343 ms
Safe context
20K
Memory
23.2 GB / 24.0 GB
Memory breakdown
Weights16.5 GB
KV Cache3.2 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsQwen3.5 27B on RTX 4500 Ada 24GB
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: 20.7 tok/s decode · 9.3s TTFT (warm) · 52 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 | 20.7 tok/s | 5096 ms | 20K |
| Coding | C | Runs with offload | 20.7 tok/s | 9343 ms | 20K |
| Agentic Coding | D | Very compromised (needs ~1.5 GB host RAM) | 12.7 tok/s | 22142 ms | 20K |
| Reasoning | C | Runs with offload | 20.7 tok/s | 11042 ms | 20K |
| RAG | D | Very compromised (needs ~1.5 GB host RAM) | 12.7 tok/s | 27678 ms | 20K |
Quantization options
How Qwen3.5 27B (27B params) fits at each quantization level on RTX 4500 Ada 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C50 |
Q3_K_S | 3 | 13.2 GB | Low | C50 |
NVFP4 | 4 | 15.1 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 16.5 GB | Medium | C50 |
Q5_K_M | 5 | 19.4 GB | High | F0 |
Q6_K | 6 | 22.1 GB | High | F0 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen3.5 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-27B-GGUF" \
--hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Hardware that runs Qwen3.5 27B well
32 GB VRAM (+8)896 GB/s (+464)
B
Raises estimated decode speed by about 121%.45.7 tok/s decode
Raises estimated decode speed by about 121%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)576 GB/s (+144)
C
Raises estimated decode speed by about 35%.28 tok/s decode
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP