MacBook Pro M3 Max 128GBOpción económica
128 GB Unified (+48)
A
Este setup está bastante equilibrado para este modelo.112.8 tok/s decodificación
~$2,499 MSRP
Can LFM2.5 8B A1B run on NVIDIA A100 80GB?
YES — Runs Great
B70Good
Estimated from fit model
LFM2.5 8B A1B needs ~14.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~805 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 14.3 GB, 804.9 tok/s, Runs well
14.3 GB required80.0 GB available
18% VRAM used
Fit status
Runs well
Decode
804.9 tok/s
TTFT
350 ms
Safe context
128K
Memory
14.3 GB / 80.0 GB
Memory breakdown
Weights5.2 GB
KV Cache0.2 GB
Runtime0.9 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsLFM2.5 8B A1B on NVIDIA A100 80GB
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: 804.9 tok/s decode · 350ms TTFT (warm) · 2012 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 | B | Runs well | 804.9 tok/s | 350 ms | 128K |
| Coding | B | Runs well | 804.9 tok/s | 350 ms | 128K |
| Agentic Coding | B | Runs well | 804.9 tok/s | 350 ms | 128K |
| Reasoning | B | Runs well | 804.9 tok/s | 350 ms | 128K |
| RAG | B | Runs well | 804.9 tok/s | 437 ms | 128K |
Inference speed
LFM2.5 8B A1B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for LFM2.5 8B A1B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~508 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 507.8 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 324.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 324.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 277.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 261.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 258.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 218.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 206.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 161.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 161.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 159.9 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 112.8 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 103.4 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 100.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 98.8 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 84.0 | Tight |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How LFM2.5 8B A1B (8.5B params) fits at each quantization level on NVIDIA A100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.3 GB | Low | B64 |
Q3_K_S | 3 | 4.2 GB | Low | B64 |
NVFP4 | 4 | 4.8 GB | Medium | B64 |
Q4_K_M | 4 | 5.2 GB | Medium | B64 |
Q5_K_M | 5 | 6.1 GB | High | B64 |
Q6_K | 6 | 7.0 GB | High | B64 |
Q8_0 | 8 | 9.1 GB | Very High | B64 |
F16Best for your GPU | 16 | 17.4 GB | Maximum | B65 |
Get started
Copy-paste commands to run LFM2.5 8B A1B on your machine.
Run
lms load LFM2.5-8B-A1B && lms server startOpciones de mejora
Hardware que ejecuta bien LFM2.5 8B A1B
Frequently asked questions
Can NVIDIA A100 80GB run LFM2.5 8B A1B?
Yes, NVIDIA A100 80GB can run LFM2.5 8B A1B with a B grade (Runs well). Expected decode speed: 804.9 tok/s.
How much VRAM does LFM2.5 8B A1B need?
LFM2.5 8B A1B (8.5B parameters) requires approximately 14.3 GB of memory with Q4_K_M quantization.
What is the best quantization for LFM2.5 8B A1B?
The recommended quantization for LFM2.5 8B A1B is Q4_K_M, which balances quality and memory efficiency.
What speed will LFM2.5 8B A1B run at on NVIDIA A100 80GB?
On NVIDIA A100 80GB, LFM2.5 8B A1B achieves approximately 804.9 tokens per second decode speed with a time-to-first-token of 350ms using Q4_K_M quantization.
Can NVIDIA A100 80GB run LFM2.5 8B A1B for coding?
For coding workloads, LFM2.5 8B A1B on NVIDIA A100 80GB receives a B grade with 804.9 tok/s and 128K context.
What context window can LFM2.5 8B A1B use on NVIDIA A100 80GB?
On NVIDIA A100 80GB, LFM2.5 8B A1B can safely use up to 128K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
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