WizardLM 13B needs ~22.6 GB but RX 7600 XT 16GB only has 16.0 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
6.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.6 tok/s
TTFT
25440 ms
Safe context
7K
Memory
22.6 GB / 16.0 GB
Offload
30%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 22.6 GB, but this setup only exposes 16.0 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 | B | Runs with offload (needs ~0.3 GB host RAM) | 14.7 tok/s | 7162 ms | 7K |
| Coding | F | Too heavy | 7.6 tok/s | 25440 ms | 7K |
| Agentic Coding | F | Too heavy | 3.2 tok/s | 89124 ms | 7K |
| Reasoning | F | Too heavy | 7.6 tok/s | 30066 ms | 7K |
| RAG | F | Too heavy | 3.2 tok/s | 111405 ms | 7K |
Inference speed
Estimated decode speed (tokens/sec) for WizardLM 13B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 151.4 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 96.6 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 87.2 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 82.6 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 70.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 58.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 55.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 38.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 38.2 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 30.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 27.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 27.1 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 23.3 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 9.5 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 6.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 3.8 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How WizardLM 13B (13B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B70 |
Q3_K_S | 3 | 6.4 GB | Low | A71 |
NVFP4 | 4 | 7.3 GB | Medium | A72 |
Q4_K_M | 4 | 7.9 GB | Medium | A72 |
Q5_K_M | 5 | 9.4 GB | High | A72 |
Q6_KBest for your GPU | 6 | 10.7 GB | High | A72 |
Q8_0 | 8 | 13.9 GB | Very High | F0 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 343%.
~$899 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.
~$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.
~$1,899 MSRP
No, WizardLM 13B requires more memory than RX 7600 XT 16GB provides.
WizardLM 13B (13B parameters) requires approximately 22.6 GB of memory with Q4_K_M quantization.
The recommended quantization for WizardLM 13B is Q4_K_M, which balances quality and memory efficiency.
On RX 7600 XT 16GB, WizardLM 13B achieves approximately 7.6 tokens per second decode speed with a time-to-first-token of 25440ms using Q4_K_M quantization.
For coding workloads, WizardLM 13B on RX 7600 XT 16GB receives a F grade with 7.6 tok/s and 7K context.
On RX 7600 XT 16GB, WizardLM 13B can safely use up to 7K tokens of context. The model's official context limit is 8K, 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/wizardlm-13b-on-rx-7600-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
