RX 7900 XT 20GB经济之选
20 GB VRAM (+4)800 GB/s (+480)
A
Raises estimated decode speed by about 108%.98 tok/s 解码
Raises estimated decode speed by about 108%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can LLaVA 1.5 7B run on RX 9060 XT 16GB?
YES — Tight Fit
B70Good
Estimated from fit model
LLaVA 1.5 7B needs ~14.6 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~47 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
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) — 14.6 GB, 47.2 tok/s, Tight fit
14.6 GB required16.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
47.2 tok/s
TTFT
4101 ms
Safe context
4K
Memory
14.6 GB / 16.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsLLaVA 1.5 7B on RX 9060 XT 16GB
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: 47.2 tok/s decode · 4.1s TTFT (warm) · 118 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 47.2 tok/s | 2237 ms | 4K |
| Coding | B | Tight fit | 47.2 tok/s | 4101 ms | 4K |
| Agentic Coding | F | Too heavy | 17.4 tok/s | 16140 ms | 4K |
| Reasoning | B | Tight fit | 47.2 tok/s | 4846 ms | 4K |
| RAG | F | Too heavy | 17.4 tok/s | 20175 ms | 4K |
Quantization options
How LLaVA 1.5 7B (7B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B65 |
Q3_K_S | 3 | 3.4 GB | Low | B65 |
NVFP4 | 4 | 3.9 GB | Medium | B66 |
Q4_K_M | 4 | 4.3 GB | Medium | B66 |
Q5_K_M | 5 | 5.0 GB | High | B67 |
Q6_K | 6 | 5.7 GB | High | B67 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B69 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run LLaVA 1.5 7B on your machine.
Run
ollama run llava升级选项
能流畅运行 LLaVA 1.5 7B 的硬件
RX 7900 XTX 24GB最佳性价比
24 GB VRAM (+8)960 GB/s (+640)
A
Raises estimated decode speed by about 108%.98 tok/s 解码
Raises estimated decode speed by about 108%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Radeon AI PRO R9700 32GBAMD 升级
32 GB VRAM (+16)640 GB/s (+320)
A
Raises estimated decode speed by about 87%.88.4 tok/s 解码
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Frequently asked questions
Can RX 9060 XT 16GB run LLaVA 1.5 7B?
Yes, RX 9060 XT 16GB can run LLaVA 1.5 7B with a B grade (Tight fit). Expected decode speed: 47.2 tok/s.
How much VRAM does LLaVA 1.5 7B need?
LLaVA 1.5 7B (7B parameters) requires approximately 14.6 GB of memory with Q4_K_M quantization.
What is the best quantization for LLaVA 1.5 7B?
The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will LLaVA 1.5 7B run at on RX 9060 XT 16GB?
On RX 9060 XT 16GB, LLaVA 1.5 7B achieves approximately 47.2 tokens per second decode speed with a time-to-first-token of 4101ms using Q4_K_M quantization.
Can RX 9060 XT 16GB run LLaVA 1.5 7B for coding?
For coding workloads, LLaVA 1.5 7B on RX 9060 XT 16GB receives a B grade with 47.2 tok/s and 4K context.
What context window can LLaVA 1.5 7B use on RX 9060 XT 16GB?
On RX 9060 XT 16GB, 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.
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