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Choose LFM2.5-24B-A2B when your priority is a model explicitly post-trained for local agents, with documented tool calling, structured agent benchmarks, mixture-of-experts efficiency and broad local-serving formats. Choose Llama 3.1 8B when you want a dense 8B baseline, Meta’s mature ecosystem and its published commercial-and-research license framework. There is no honest universal speed or quality winner: the available figures come from different test suites and configurations, so run both checkpoints through the same agent harness on your hardware before committing.
Quick comparison
| Question | LFM2.5-24B-A2B | Llama 3.1 8B |
|---|---|---|
| Architecture | 24.0B total parameters; 2.45B active parameters; mixture of experts with 40 layers and 64 experts per MoE block, top-4 routing | Dense 8B model |
| Published context | 131,072 tokens | 128K tokens for the Llama 3.1 family |
| Agent focus | Explicitly post-trained for agentic workloads and documented tool calling | Instruction model supports assistant-style use; Meta publishes capability evaluations including tool calls, but not the same agent benchmark table |
| Agent benchmark evidence | AutomationBench 27.69; BFCLv4 65.01; ToolSandbox 80.71; LiveClaw 48.09; PinchBench 83.18; τ³-Bench Banking 8.04; WebArena Verified 26.75 | No directly matching results in the cited model-card material |
| Local formats and servers | Transformers, vLLM, SGLang, Docker, OpenAI-compatible APIs, GGUF, ONNX, MLX 4-bit, llama.cpp-compatible tools, Ollama and LM Studio | Meta’s established ecosystem; confirm the exact checkpoint conversion and runtime support for your selected stack |
| Published edge speed | Liquid AI reports 184 tokens/s on Apple M5 Max and 114 tokens/s on AMD Ryzen AI Max+ 395, using llama.cpp, Q4_K_M and a 4K-token input context | No same-hardware, same-quantization figure is supplied here |
| License information | Check the current LFM2.5 model card and distribution terms for your checkpoint | Llama 3.1 Community License and Acceptable Use Policy |
The numbers in this table are not a league table. LFM2.5’s agent scores and Llama 3.1’s general-language scores measure different tasks, prompts and evaluation procedures.
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What LFM2.5-24B-A2B is
Liquid AI describes LFM2.5-24B-A2B as a 24B-parameter reasoning model with only about 2B active parameters for fast, private on-device agents. The detailed model specification gives 24.0B total parameters and 2.45B active parameters. It has 40 layers, with 64 experts in each mixture-of-experts block and top-4 routing. At each token, routing activates only a subset of the available weights; that is why the model can have a much larger total capacity than a dense model with a similar per-token compute budget.
The model was trained on 36 trillion tokens and lists a 131,072-token context window. Its stated language coverage is 16 languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian and Polish. Those language claims describe coverage, not equal quality in every language or every tool-use scenario.
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Agent use is a first-class goal rather than an inference made from a general chat model. The card explicitly says that LFM2.5-24B-A2B supports tool calling for agentic workflows. It also provides multiple deployment routes: Transformers, vLLM, SGLang, Docker and OpenAI-compatible APIs, plus GGUF for llama.cpp-compatible tools, ONNX for cross-platform deployment and MLX 4-bit for Apple Silicon. Ollama and LM Studio are listed among compatible local tools.
What Llama 3.1 8B is
Llama 3.1 8B is Meta’s dense eight-billion-parameter model in the Llama 3.1 family, specified at 128K context. The instruction-tuned checkpoint is intended for assistant-like conversations; the pretrained checkpoint can be adapted to other language-generation tasks. A dense architecture activates its full parameter set for each token, which makes memory and compute behavior easier to reason about than an MoE model, although the smaller total parameter count does not by itself guarantee better answers or lower latency on every device.
Meta publishes separate results for pretrained and instruction-tuned variants. For the pretrained 8B model, the cited scores include MMLU 66.7, MMLU-Pro (CoT) 37.1, AGIEval English 47.8, CommonSenseQA 75.0 and Winogrande 60.5. For the instruction-tuned 8B model, the cited results include MMLU 69.4, MMLU (CoT) 73.0, MMLU-Pro (CoT) 48.3 and IFEval 80.4. These are useful indicators of general knowledge, reasoning and instruction following, but they cannot be converted into a direct ranking against LFM2.5’s agent-suite scores.
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LFM2.5 has the stronger evidence for this specific requirement because tool calling is an explicit training target and the model card reports several agent evaluations. The published figures are:
| Evaluation | LFM2.5-24B-A2B result | What to keep in mind |
|---|---|---|
| AutomationBench | 27.69 | Use the score only within that benchmark’s definition and setup |
| BFCLv4 | 65.01 | Tests function-calling behavior under the benchmark’s own prompts and grading |
| ToolSandbox | 80.71 | Not interchangeable with BFCLv4 or general instruction scores |
| LiveClaw | 48.09 | Reflects that benchmark’s live-agent tasks |
| PinchBench | 83.18 | Benchmark-specific tool and workflow result |
| τ³-Bench Banking | 8.04 | Banking-domain evaluation; do not generalize to every tool domain |
| WebArena Verified | 26.75 | Reported with temperature 0.6 and up to 40 browser actions |
The WebArena condition matters: changing temperature, action limits, browser state or tool schemas can materially change an agent score. The same caution applies to every benchmark in the table. Meta’s card discusses capability evaluations that include tool calls, long context, multilingual tasks, coding and memorization, but the supplied material does not provide a matching table of Llama 3.1 8B agent scores. That is an evidence gap, not proof that Llama cannot call tools.
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For a production agent, measure more than whether a function name is emitted. Record valid JSON rate, schema adherence, correct argument values, refusal or recovery after a tool error, number of unnecessary calls, final-answer correctness and tokens spent per completed task. Use identical system prompts, tool definitions, temperature, context, retry policy and stopping rules for both models.
Reasoning and structured output
LFM2.5’s “reasoning model” positioning and agent-specific post-training make it the better-informed first trial for multi-step local workflows. Its benchmark portfolio includes browser, banking, sandbox and automation tasks, which are closer to an agent loop than a single-turn knowledge test.
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Llama 3.1 8B has the advantage of clearly published general-language and instruction-following results. The instruction model’s MMLU score of 69.4, MMLU chain-of-thought score of 73.0, MMLU-Pro chain-of-thought score of 48.3 and IFEval score of 80.4 are useful baselines when your workload is mainly chat, extraction, classification or text generation. They do not establish how it will perform on your particular function schemas.
Neither set of figures guarantees reliable structured output. A robust application should enforce a JSON schema at the API boundary, validate every response, reject unknown fields, set bounded retries and provide a recovery message when a call is malformed. Keep tool permissions narrow: a model that produces syntactically valid arguments can still choose an unsafe operation or an incorrect target.
Speed, first-token latency and throughput
Liquid AI reports 184 tokens per second on an Apple M5 Max and 114 tokens per second on an AMD Ryzen AI Max+ 395. Both figures use llama.cpp, Q4_K_M quantization and a 4K-token input context. They are vendor measurements, not an independent head-to-head comparison with Llama 3.1 8B. Liquid AI also reports up to 21K output tokens per second on one NVIDIA B300 in a separate BF16, high-concurrency setup; that server result should not be compared with the edge numbers.
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No first-token latency comparison for these exact checkpoints is provided. Throughput and time to first token can move in opposite directions: a long prompt, prompt processing, MoE routing, CPU-GPU offload, batch size, quantization, thermals and context-cache state all matter. For an interactive assistant, measure p50 and p95 time to first token and time to complete a representative tool call, not just streaming output tokens per second.
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On Apple Silicon, LFM2.5 has a specifically listed MLX 4-bit path as well as llama.cpp-compatible GGUF support. That makes it straightforward to test two back ends, but results from MLX and llama.cpp are not interchangeable. On AMD hardware, the cited 114-token-per-second figure is a useful reference point only if your processor, quantization, context and build are close to the stated setup.
Memory footprint, context and quantization
The available data does not state a minimum RAM or VRAM requirement for either model, and it does not provide a same-quantization quality study. Do not turn parameter counts into a guaranteed hardware number. Runtime overhead, the key-value cache for long contexts, allocator behavior and whether weights are split between system memory and GPU memory can change the requirement substantially.
LFM2.5 stores 24.0B total parameters even though only 2.45B are active for each token. Quantization therefore reduces the weight-storage cost, but it does not make the model equivalent to a dense 2.45B checkpoint. The published edge measurement uses Q4_K_M, while the server measurement uses BF16; those are different memory and quality points.
Llama 3.1 8B has fewer total parameters and is therefore the simpler starting point when your device has a tight memory budget. Its 128K family context and LFM2.5’s 131,072-token context are both theoretical maxima for a deployment: usable context also depends on the runtime, cache allocation and application prompt. Long context increases memory use and usually increases prompt-processing time.
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For a fair quantization test, keep the quantization family, context length and runtime constant. Compare task success, malformed-tool rate and answer quality at the same memory ceiling. A lower-bit model that completes an agent task reliably can be more useful than a higher-precision model that repeatedly exceeds your latency or memory budget.
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Using LFM2.5 locally
- Select the official checkpoint format that matches your stack: Transformers, vLLM, SGLang, Docker/OpenAI-compatible serving, GGUF for llama.cpp-compatible tools, ONNX for cross-platform inference or MLX 4-bit for Apple Silicon.
- For Ollama or LM Studio, use the corresponding LFM2.5 package or imported GGUF and confirm the model’s context setting, GPU offload and tool-calling template.
- Expose tools through a strict schema and test a no-tool response, a single valid call, a multi-step sequence and a simulated tool error before connecting real credentials.
- Record prompt length, output length, first-token latency, completed-task latency, peak memory and malformed-call rate.
Using Llama 3.1 8B
- Choose the pretrained or instruction-tuned checkpoint deliberately; assistant agents normally start with the instruction-tuned variant.
- Verify that your selected local runtime supports the exact checkpoint and chat or function-calling template. Do not assume that a generic Llama template preserves the model’s intended formatting.
- Apply the same tool schema, decoding settings, context limit and retry policy used for LFM2.5.
- Evaluate the same task set and retain failure transcripts so you can distinguish model errors from parser, template or runtime errors.
LFM2.5 has more explicitly documented serving formats in the supplied model information. Llama 3.1 benefits from Meta’s established ecosystem, but the practical result still depends on the particular quantization, converter and front end you select.
Licensing and safety for commercial agents
Meta states that Llama 3.1 is intended for commercial and research use under the Llama 3.1 Community License and Acceptable Use Policy. Read the current license, comply with its redistribution and attribution requirements, and check whether your product or deployment triggers any additional conditions. Meta also says that large language models, including Llama 3.1, are not designed to be deployed in isolation and should be part of an overall AI system with additional safety guardrails as required.
The supplied LFM2.5 facts do not specify its license obligations. Before shipping a commercial product, inspect the current LFM2.5 model card and the exact distribution you plan to use; do not infer terms from the model’s open formats or from another Liquid AI release.
For either model, put authorization outside the model. Use per-tool allowlists, least-privilege credentials, argument validation, rate limits, audit logs, human approval for irreversible actions and isolation for browser or shell tools. Add prompt-injection defenses when an agent reads web pages, documents or email. A local model improves data locality, but it does not remove the need for system-level controls.
Which model fits common scenarios?
Private browser, desktop or automation agent
Start with LFM2.5-24B-A2B. Its explicit agent training, tool-calling support and WebArena Verified result make it the more evidence-backed candidate. Re-test with your browser action budget and temperature because the published WebArena figure used temperature 0.6 and up to 40 actions.
Small-memory laptop or CPU-first deployment
Start with Llama 3.1 8B when the primary constraint is total weight storage and you prefer a dense model with predictable activation. Test LFM2.5 as well if its Q4_K_M performance on your hardware is attractive; MoE active-parameter efficiency can change the practical trade-off.
Apple Silicon
LFM2.5 has a documented MLX 4-bit path and a vendor-reported 184-token-per-second M5 Max result under specified conditions. Treat that as a reference, then measure your own prompt lengths and thermal behavior. Llama 3.1 remains a useful baseline for ecosystem compatibility and quality.
General assistant, writing or knowledge tasks
Llama 3.1 8B’s published MMLU and IFEval results provide a clearer general-purpose baseline. Choose the instruction-tuned checkpoint for assistant behavior and validate any structured-output requirement separately.
Multilingual agent
LFM2.5 explicitly lists 16 languages, including English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian and Polish. Verify quality on your own domain vocabulary and tool names; a language appearing on a support list does not establish equal agent reliability.
A fair bake-off procedure
- Freeze the task set: include ordinary chat, extraction, one-tool calls, multi-tool plans, invalid arguments, tool failures, prompt injection and long-context retrieval.
- Use matched decoding settings, identical tool schemas, the same context budget and the same runtime class where possible.
- Test at the quantization you can actually deploy, recording peak RAM or VRAM and thermal throttling rather than relying on nominal specifications.
- Score task completion, valid-schema rate, argument accuracy, unnecessary calls, recovery after errors, time to first token, end-to-end latency and output-token cost.
- Repeat enough times to expose stochastic failures. Report medians and tail latency, not a single best run.
- Review transcripts manually for unsafe actions, hidden assumptions and plausible but incorrect final answers.
Evidence limits you should keep in mind
No same-hardware, same-quantization, same-prompt benchmark for these exact two checkpoints is established in the cited material. Liquid AI’s throughput figures are vendor measurements with stated hardware, Q4_K_M quantization, llama.cpp and a 4K input context. Llama 3.1’s published scores come from Meta’s internal evaluation library and cover different tasks from LFM2.5’s agent suite. Treat every cross-model conclusion as a deployment hypothesis until your own harness confirms it.
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