Reviewed repository / hardware evidence
LFM2.5-2.6B is the small-model case in this reviewed batch. The Hub reports 2.70 billion safetensors parameters, while its card labels it for conversational and edge use across a multilingual language set. That makes the useful question less about multi-GPU sharding and more about how much memory remains after weights for cache, runtime workspace, and the operating system.
See the precision plan → Inspect the source repository →Decision summary
The weight-plus-20%-headroom floors are 1.51 GiB at INT4, 3.01 GiB at INT8, and 6.03 GiB at BF16. All three clear a conservative 7.2 GiB usable budget on an 8 GB card, but BF16 leaves little room for everything the formula intentionally omits. INT4 or INT8 is the more defensible starting point for an 8 GB deployment experiment.
Why this page exists. This page earns a standalone URL because it resolves a real boundary: unlike generic 7B guidance, the source-backed 2.70B total makes even a BF16 weight floor plausible on 8 GB, while still showing why weight fit alone is not deployment proof.
Weights first / precision by precision
Every row uses 2.7B parameters × bits ÷ 8 ÷ 1024³, then adds 20% planning headroom. The single-card tier assumes 90% of labeled VRAM is available.
| Mode | Bits | Raw weights | + 20% floor | First single-card tier | 24 GB GPU count* | Calculator |
|---|---|---|---|---|---|---|
| INT4 | 4 bits | 1.26 GiB | 1.51 GiB | 8 GB | 1 | Edit plan → |
| INT8 | 8 bits | 2.51 GiB | 3.01 GiB | 8 GB | 1 | Edit plan → |
| BF16 | 16 bits | 5.02 GiB | 6.03 GiB | 8 GB | 1 | Edit plan → |
*GPU count is capacity division against 90% usable VRAM. It does not prove the runtime can shard the model, avoid replication, or deliver acceptable interconnect performance.
INT4 card-fit matrix
Positive margin means the INT4 weight-plus-headroom floor fits inside the declared usable budget. It is not leftover context capacity: cache, runtime workspace, and system allocations are still unknown.
| Card memory | 90% usable | One-card result | Capacity-only count |
|---|---|---|---|
| 8 GB | 7.2 GiB | Clears by 5.69 GiB | 1 |
| 12 GB | 10.8 GiB | Clears by 9.29 GiB | 1 |
| 16 GB | 14.4 GiB | Clears by 12.89 GiB | 1 |
| 24 GB | 21.6 GiB | Clears by 20.09 GiB | 1 |
| 32 GB | 28.8 GiB | Clears by 27.29 GiB | 1 |
| 48 GB | 43.2 GiB | Clears by 41.69 GiB | 1 |
| 80 GB | 72 GiB | Clears by 70.49 GiB | 1 |
| 96 GB | 86.4 GiB | Clears by 84.89 GiB | 1 |
| 141 GB | 126.9 GiB | Clears by 125.4 GiB | 1 |
Public source record
Metadata refreshed Aug 15, 2026. Open the source before relying on any field.
Open LiquidAI/LFM2.5-2.6B →Do not confuse capacity with deployment
Repository-specific caution.
The repository declares a non-standard LFM license rather than MIT or Apache. Memory fit does not answer whether a runtime supports the Lfm2ForCausalLM architecture, whether the chosen artifact matches the parameter total, or whether a particular context window will remain inside the card. Check the model card, license, and exact checkpoint before downloading.
Formula boundary.
No KV cache, activations, graph capture, allocator fragmentation, offload, operating-system reserve, throughput, latency, or power is included.
Source boundary.
Hub trend, downloads, and likes help discover repositories. They are not a benchmark, endorsement, security review, or license opinion.
Passive backlink / model-card distribution
Add this passive SVG badge to a README, model card, documentation page, or benchmark report. It states the source-backed LFM2.5-2.6B INT4 floor and links to the assumptions—not to a download or quality claim.
Why this repository benefits. This badge belongs beside an edge-device or 8 GB deployment note: it makes the unusually small 1.51 GiB INT4 floor visible while the linked page explains why BF16's apparent 8 GB fit still needs cache and runtime measurement.
Badge visits use utm_source=model_badge, utm_medium=model_card, and this repository’s slug. Copying does not submit or store anything.
Nearest reviewed parameter totals
These pages use the same formula and source rules, making the capacity boundary comparable without pretending the models have equivalent quality or runtime behavior.
LFM2.5-2.6B FAQ
Use these as a first-pass hardware screen. Replace parameter arithmetic with exact artifact bytes and architecture-aware cache inputs before deployment.
Open the editable GPU calculator →LFM2.5-2.6B has 2.7 billion safetensors parameters in the public Hub metadata. The weight-plus-20%-headroom floors are 1.51 GiB at INT4, 3.01 GiB at INT8, and 6.03 GiB at BF16. These estimates exclude KV cache, activations, runtime workspace, fragmentation, offload, and speed.
The first listed card tier whose 90%-usable budget clears the INT4 weight floor is 8 GB. That is an arithmetic starting point, not a runtime guarantee.
No. Downloads, likes, and trending position are discovery signals. They do not prove task quality, safety, speed, runtime compatibility, or licensing suitability.
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