LLaVA 1.5 7B needs ~14.2 GB VRAM. Radeon RX 7800M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~32 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
2.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.7 GB host RAM)
Decode
31.5 tok/s
TTFT
6148 ms
Safe context
4K
Memory
14.2 GB / 12.0 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 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 59.7 tok/s | 1769 ms | 4K |
| Coding | C | Very compromised (needs ~0.7 GB host RAM) | 31.5 tok/s | 6148 ms | 4K |
| Agentic Coding | F | Too heavy | 12.5 tok/s | 22521 ms | 4K |
| Reasoning | C | Very compromised (needs ~0.7 GB host RAM) | 31.5 tok/s | 7265 ms | 4K |
| RAG | F | Too heavy | 12.5 tok/s | 28151 ms |
How LLaVA 1.5 7B (7B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B68 |
NVFP4 | 4 |
Copy-paste commands to run LLaVA 1.5 7B on your machine.
Run
ollama run llavaUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 50%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 195%.
~$479 MSRP
Yes, Radeon RX 7800M 12GB can run LLaVA 1.5 7B with a C grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 31.5 tok/s.
LLaVA 1.5 7B (7B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.
The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.
On Radeon RX 7800M 12GB, LLaVA 1.5 7B achieves approximately 31.5 tokens per second decode speed with a time-to-first-token of 6148ms using Q4_K_M quantization.
For coding workloads, LLaVA 1.5 7B on Radeon RX 7800M 12GB receives a C grade with 31.5 tok/s and 4K context.
On Radeon RX 7800M 12GB, LLaVA 1.5 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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/llava-1.5-7b-on-rx-7800m-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4K |
3.9 GB |
| Medium |
| B68 |
Q4_K_M | 4 | 4.3 GB | Medium | B69 |
Q5_K_M | 5 | 5.0 GB | High | B70 |
Q6_K | 6 | 5.7 GB | High | A70 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B70 |
F16 | 16 | 14.3 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.