RX 7600 XT 16GB最佳性价比
288 GB/s (+138)
C
Raises estimated decode speed by about 52%.25.6 tok/s 解码
Raises estimated decode speed by about 52%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can solar finalised finetuned Model 10.7B i1 run on MacBook Pro M3 Pro 18GB?
YES — Runs Great
C51Usable
Estimated from fit model
solar finalised finetuned Model 10.7B i1 needs ~10.6 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~17 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 10.6 GB, 16.8 tok/s, Runs well
10.6 GB required13.0 GB available
82% VRAM used
Fit status
Runs well
Decode
16.8 tok/s
TTFT
11540 ms
Safe context
46K
Memory
10.6 GB / 13.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.9 GB
See how fast it feels
See how fast it feelssolar finalised finetuned Model 10.7B i1 on MacBook Pro M3 Pro 18GB
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: 16.8 tok/s decode · 11.5s TTFT (warm) · 42 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 16.8 tok/s | 6295 ms | 46K |
| Coding | C | Runs well | 16.8 tok/s | 11540 ms | 46K |
| Agentic Coding | C | Tight fit | 16.8 tok/s | 16785 ms | 46K |
| Reasoning | C | Runs well | 16.8 tok/s | 13638 ms | 46K |
| RAG | C | Tight fit | 16.8 tok/s | 20982 ms | 46K |
Quantization options
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C50 |
Q3_K_S | 3 | 5.2 GB | Low | C51 |
NVFP4 | 4 | 6.0 GB | Medium | C52 |
Q4_K_M | 4 | 6.5 GB | Medium | C52 |
Q5_K_M | 5 | 7.7 GB | High | C51 |
Q6_KBest for your GPU | 6 | 8.8 GB | High | C51 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Get started
Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.
Run
lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start升级选项
能流畅运行 solar finalised finetuned Model 10.7B i1 的硬件
Frequently asked questions
Can MacBook Pro M3 Pro 18GB run solar finalised finetuned Model 10.7B i1?
Yes, MacBook Pro M3 Pro 18GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 16.8 tok/s.
How much VRAM does solar finalised finetuned Model 10.7B i1 need?
solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
What is the best quantization for solar finalised finetuned Model 10.7B i1?
The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will solar finalised finetuned Model 10.7B i1 run at on MacBook Pro M3 Pro 18GB?
On MacBook Pro M3 Pro 18GB, solar finalised finetuned Model 10.7B i1 achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11540ms using Q4_K_M quantization.
Can MacBook Pro M3 Pro 18GB run solar finalised finetuned Model 10.7B i1 for coding?
For coding workloads, solar finalised finetuned Model 10.7B i1 on MacBook Pro M3 Pro 18GB receives a C grade with 16.8 tok/s and 46K context.
What context window can solar finalised finetuned Model 10.7B i1 use on MacBook Pro M3 Pro 18GB?
On MacBook Pro M3 Pro 18GB, solar finalised finetuned Model 10.7B i1 can safely use up to 46K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M3 Pro 18GB as fast as VRAM for solar finalised finetuned Model 10.7B i1?
Not always. MacBook Pro M3 Pro 18GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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