RTX 3080 Ti 12GBMaior salto912 GB/s (+712)
B
Raises estimated decode speed by about 372%.106.3 tok/s decodificação
Raises estimated decode speed by about 372%.
~$1,199 MSRP
Can Helply 10.2b chat i1 run on MacBook Pro M2 Pro 16GB?
YES — Tight Fit
C49Usable
Estimated from fit model
Helply 10.2b chat i1 needs ~10.0 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~23 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) — 10.0 GB, 22.5 tok/s, Tight fit
10.0 GB required11.5 GB available
87% VRAM used
Fit status
Tight fit
Decode
22.5 tok/s
TTFT
8604 ms
Safe context
36K
Memory
10.0 GB / 11.5 GB
Memory breakdown
Weights6.2 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom1.7 GB
See how fast it feels
See how fast it feelsHelply 10.2b chat i1 on MacBook Pro M2 Pro 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: 22.5 tok/s decode · 8.6s TTFT (warm) · 56 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 | Tight fit | 22.5 tok/s | 4693 ms | 36K |
| Coding | C | Tight fit | 22.5 tok/s | 8604 ms | 36K |
| Agentic Coding | C | Runs with offload | 22.5 tok/s | 12515 ms | 36K |
| Reasoning | C | Tight fit | 22.5 tok/s | 10169 ms | 36K |
| RAG | C | Runs with offload | 22.5 tok/s | 15644 ms | 36K |
Quantization options
How Helply 10.2b chat i1 (10.199999809265137B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.0 GB | Low | C51 |
Q3_K_S | 3 | 5.0 GB | Low | C52 |
NVFP4 | 4 | 5.7 GB | Medium | C52 |
Q4_K_M | 4 | 6.2 GB | Medium | C52 |
Q5_K_M | 5 | 7.3 GB | High | C51 |
Q6_KBest for your GPU | 6 | 8.4 GB | High | C51 |
Q8_0 | 8 | 10.9 GB | Very High | F0 |
F16 | 16 | 20.9 GB | Maximum | F0 |
Get started
Copy-paste commands to run Helply 10.2b chat i1 on your machine.
Run
lms load hf-mradermacher--helply-10-2b-chat-i1-gguf && lms server startOpções de upgrade
Hardware que roda bem Helply 10.2b chat i1
RTX 3080 Ti 12GBMaior saltoMacBook Pro M4 Pro 24GBOpção econômica
24 GB Unified (+8)273 GB/s (+73)
C
Raises estimated decode speed by about 32%.29.7 tok/s decodificação
Raises estimated decode speed by about 32%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
MacBook Pro M3 Pro 18GBMelhor custo-benefício
18 GB Unified (+2)
C
This setup is broadly balanced for this model.17.6 tok/s decodificação
~$1,999 MSRP
MacBook Pro M2 Max 32GBUpgrade Apple
32 GB Unified (+16)400 GB/s (+200)
C
Raises estimated decode speed by about 66%.37.3 tok/s decodificação
Raises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Frequently asked questions
Can MacBook Pro M2 Pro 16GB run Helply 10.2b chat i1?
Yes, MacBook Pro M2 Pro 16GB can run Helply 10.2b chat i1 with a C grade (Tight fit). Expected decode speed: 22.5 tok/s.
How much VRAM does Helply 10.2b chat i1 need?
Helply 10.2b chat i1 (10.199999809265137B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.
What is the best quantization for Helply 10.2b chat i1?
The recommended quantization for Helply 10.2b chat i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will Helply 10.2b chat i1 run at on MacBook Pro M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Helply 10.2b chat i1 achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8604ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 16GB run Helply 10.2b chat i1 for coding?
For coding workloads, Helply 10.2b chat i1 on MacBook Pro M2 Pro 16GB receives a C grade with 22.5 tok/s and 36K context.
What context window can Helply 10.2b chat i1 use on MacBook Pro M2 Pro 16GB?
On MacBook Pro M2 Pro 16GB, Helply 10.2b chat i1 can safely use up to 36K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Helply 10.2b chat i1?
Not always. MacBook Pro M2 Pro 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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