MacBook Pro M4 32GB经济之选
32 GB Unified (+16)120 GB/s (+52)
C
Raises estimated decode speed by about 84%.11.6 tok/s 解码
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$799 MSRP
Can solar finalised finetuned Model 10.7B i1 run on MacBook Air M1 16GB?
YES — Tight Fit
C45Usable
Estimated from fit model
solar finalised finetuned Model 10.7B i1 needs ~10.4 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~6 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: 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) — 10.4 GB, 6.3 tok/s, Tight fit
10.4 GB required11.5 GB available
90% VRAM used
Fit status
Tight fit
Decode
6.3 tok/s
TTFT
30971 ms
Safe context
30K
Memory
10.4 GB / 11.5 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelssolar finalised finetuned Model 10.7B i1 on MacBook Air M1 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: 6.3 tok/s decode · 31.0s TTFT (warm) · 16 tok/s prefill
What limits this setup
The model fits in shared memory, but shared-memory bandwidth is now the real limiter.
Fit does not mean dedicated-VRAM speed
Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.
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
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 6.3 tok/s | 16893 ms | 30K |
| Coding | C | Tight fit | 6.3 tok/s | 30971 ms | 30K |
| Agentic Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 6.1 tok/s | 46420 ms | 30K |
| Reasoning | C | Tight fit | 6.3 tok/s | 36602 ms | 30K |
| RAG | C | Runs with offload (needs ~0.1 GB host RAM) | 6.1 tok/s | 58025 ms | 30K |
Quantization options
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C51 |
Q3_K_S | 3 | 5.2 GB | Low | C52 |
NVFP4 | 4 | 6.0 GB | Medium | C52 |
Q4_K_M | 4 | 6.5 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 7.7 GB | High | C51 |
Q6_K | 6 | 8.8 GB | High | F0 |
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
RTX 4080 Super 16GB最大飞跃736 GB/s (+668)
B
Raises estimated decode speed by about 1460%.98.3 tok/s 解码
Raises estimated decode speed by about 1460%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
MacBook Air M4 24GB最佳性价比
24 GB Unified (+8)120 GB/s (+52)
C
Raises estimated decode speed by about 84%.11.6 tok/s 解码
Raises estimated decode speed by about 84%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
MacBook Pro M3 24GBApple 升级
24 GB Unified (+8)100 GB/s (+32)
C
Raises estimated decode speed by about 65%.10.4 tok/s 解码
Raises estimated decode speed by about 65%.
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Frequently asked questions
Can MacBook Air M1 16GB run solar finalised finetuned Model 10.7B i1?
Yes, MacBook Air M1 16GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 6.3 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 10.4 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 Air M1 16GB?
On MacBook Air M1 16GB, solar finalised finetuned Model 10.7B i1 achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30971ms using Q4_K_M quantization.
Can MacBook Air M1 16GB run solar finalised finetuned Model 10.7B i1 for coding?
For coding workloads, solar finalised finetuned Model 10.7B i1 on MacBook Air M1 16GB receives a C grade with 6.3 tok/s and 30K context.
What context window can solar finalised finetuned Model 10.7B i1 use on MacBook Air M1 16GB?
On MacBook Air M1 16GB, solar finalised finetuned Model 10.7B i1 can safely use up to 30K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if solar finalised finetuned Model 10.7B i1 feels slow on MacBook Air M1 16GB?
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
Is unified memory on MacBook Air M1 16GB as fast as VRAM for solar finalised finetuned Model 10.7B i1?
Not always. MacBook Air M1 16GB 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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