Can LFM2.5 8B A1B run on RTX 4060 Ti 8GB?

YES — Tight Fit

A78Great

Estimated — low-sample bucket· few comparable runs

LFM2.5 8B A1B needs ~7.1 GB VRAM. RTX 4060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~89 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.1 GB, 88.9 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

88.9 tok/s

TTFT

2177 ms

Safe context

97K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights5.2 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsLFM2.5 8B A1B on RTX 4060 Ti 8GB
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: 88.9 tok/s decode · 2.2s TTFT (warm) · 222 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	A	Tight fit	88.9 tok/s	1188 ms	89K
Coding	A	Tight fit	88.9 tok/s	2177 ms	97K
Agentic Coding	A	Tight fit	88.9 tok/s	3167 ms	97K
Reasoning	A	Tight fit	88.9 tok/s	2573 ms	97K
RAG	A	Tight fit	88.9 tok/s	3959 ms	97K

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 RTX 4060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.3 GB	Low	A78
Q3_K_S	3	4.2 GB	Low	A77
NVFP4	4	4.8 GB	Medium	A77
Q4_K_MBest for your GPU	4	5.2 GB	Medium	A77
Q5_K_M	5	6.1 GB	High	F0
Q6_K	6	7.0 GB	High	F0
Q8_0	8	9.1 GB	Very High	F0
F16	16	17.4 GB	Maximum	F0

Get started

Copy-paste commands to run LFM2.5 8B A1B on your machine.

Run

lms load LFM2.5-8B-A1B && lms server start

Your hardware

More models your RTX 4060 Ti 8GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3.5 9B	9B	A	20.3 tok/s
CodeGeeX 4 9B	9B	A	38.5 tok/s

Frequently asked questions

Can RTX 4060 Ti 8GB run LFM2.5 8B A1B?

Yes, RTX 4060 Ti 8GB can run LFM2.5 8B A1B with a A grade (Tight fit). Expected decode speed: 88.9 tok/s.

How much VRAM does LFM2.5 8B A1B need?

LFM2.5 8B A1B (8.5B parameters) requires approximately 7.1 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 RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, LFM2.5 8B A1B achieves approximately 88.9 tokens per second decode speed with a time-to-first-token of 2177ms using Q4_K_M quantization.

Can RTX 4060 Ti 8GB run LFM2.5 8B A1B for coding?

For coding workloads, LFM2.5 8B A1B on RTX 4060 Ti 8GB receives a A grade with 88.9 tok/s and 97K context.

What context window can LFM2.5 8B A1B use on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, LFM2.5 8B A1B can safely use up to 97K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 8GB See all hardware for LFM2.5 8B A1B

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