MacBook Pro M4 Max 48GBOpción económica
48 GB Unified (+16)
C
Este setup está bastante equilibrado para este modelo.46.4 tok/s decodificación
~$2,499 MSRP
Can solar finalised finetuned Model 10.7B i1 run on Radeon Pro W7800 32GB?
YES — Runs Great
C48Usable
Estimated from fit model
solar finalised finetuned Model 10.7B i1 needs ~11.9 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 11.9 GB, 52.1 tok/s, Runs well
11.9 GB required32.0 GB available
37% VRAM used
Fit status
Runs well
Decode
52.1 tok/s
TTFT
3718 ms
Safe context
273K
Memory
11.9 GB / 32.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelssolar finalised finetuned Model 10.7B i1 on Radeon Pro W7800 32GB
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: 52.1 tok/s decode · 3.7s TTFT (warm) · 130 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 | C | Runs well | 52.1 tok/s | 2028 ms | 273K |
| Coding | C | Runs well | 52.1 tok/s | 3718 ms | 273K |
| Agentic Coding | C | Runs well | 52.1 tok/s | 5408 ms | 273K |
| Reasoning | C | Runs well | 52.1 tok/s | 4394 ms | 273K |
| RAG | C | Runs well | 52.1 tok/s | 6761 ms | 273K |
Quantization options
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C43 |
Q3_K_S | 3 | 5.2 GB | Low | C43 |
NVFP4 | 4 | 6.0 GB | Medium | C44 |
Q4_K_M | 4 | 6.5 GB | Medium | C44 |
Q5_K_M | 5 | 7.7 GB | High | C44 |
Q6_K | 6 | 8.8 GB | High | C45 |
Q8_0 | 8 | 11.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 21.9 GB | Maximum | C49 |
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 startOpciones de mejora
Hardware que ejecuta bien solar finalised finetuned Model 10.7B i1
Mac Studio M2 Ultra 64GBMejor relación calidad-precio
64 GB Unified (+32)800 GB/s (+224)
C
Sube la velocidad estimada de decodificación alrededor de un 36%.71.1 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 36%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$3,999 MSRP
Frequently asked questions
Can Radeon Pro W7800 32GB run solar finalised finetuned Model 10.7B i1?
Yes, Radeon Pro W7800 32GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 52.1 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 11.9 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 Radeon Pro W7800 32GB?
On Radeon Pro W7800 32GB, solar finalised finetuned Model 10.7B i1 achieves approximately 52.1 tokens per second decode speed with a time-to-first-token of 3718ms using Q4_K_M quantization.
Can Radeon Pro W7800 32GB run solar finalised finetuned Model 10.7B i1 for coding?
For coding workloads, solar finalised finetuned Model 10.7B i1 on Radeon Pro W7800 32GB receives a C grade with 52.1 tok/s and 273K context.
What context window can solar finalised finetuned Model 10.7B i1 use on Radeon Pro W7800 32GB?
On Radeon Pro W7800 32GB, solar finalised finetuned Model 10.7B i1 can safely use up to 273K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-radeon-pro-w7800-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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