Llama 3.3 70B Instruct vs LFM2.5 2.6B
Llama 3.3 70B Instruct and LFM2.5 2.6B both score 8 on the intelligence index. The cheapest machine here that runs Llama 3.3 70B Instruct is the Framework Desktop, 128GB, at $3,449. LFM2.5 2.6B runs on the Mac mini M6, 16GB at $899, $2,550 less. On the Framework Desktop, 128GB, the cheapest machine here that runs both, LFM2.5 2.6B is about 21.8× quicker: 87 tok/s against 4, Llama 3.3 70B Instruct's measured and the other estimated from memory bandwidth. At 500k tokens a day the Framework Desktop, 128GB pays for itself in 53 years running LFM2.5 2.6B, against 1,274 years running Llama 3.3 70B Instruct.
| Llama 3.3 70B Instruct | LFM2.5 2.6B | |
|---|---|---|
| Intelligence index | 8 | 8 |
| Class | Below every hosted tier | Below every hosted tier |
| Weights | 43 GB | 1.7 GB |
| Needs at 32k | 53 GB | 2.2 GB |
| Quantisation | Q4_K_M | Q4_K_M |
| Parameters | 70.6B | 2.7B |
| Max context | 128k | 128k |
| API price per 1M | $0.1 in / $0.32 out | $0.35 in / $0.5 out |
| Licence | Llama 3.3 Community License | LFM 1.0 (see repo) |
| Machines here that run it | 13 of 37 | 37 of 37 |
| Cheapest machine that runs it | Strix Halo Framework Desktop, 128GB $3,449 | Mac mini M6, 16GB $899 |
Run Llama 3.3 70B Instruct on the Framework Desktop, 128GB · or LFM2.5 2.6B on the Mac mini M6, 16GB
Jump to: Side by side on the Framework Desktop, 128GB · How much use it takes to pay for the machine · Machines that run one and not the other · The assumptions behind both columns
Side by side on the Framework Desktop, 128GB
The table above gives each model the cheapest machine that runs it, and those are two different machines, so nothing in it is a like-for-like race. The Strix Halo Framework Desktop, 128GB is the cheapest machine here that runs both, so this is the pair doing the same work on the same hardware.
| Llama 3.3 70B Instruct | LFM2.5 2.6B | |
|---|---|---|
| Speed at 32k | 4 tok/s measured | 87 tok/s estimated |
| Pay-back on this machine | Pays back in 1,274 years | Pays back in 53 years |
| API cost per month | $1.73 | $5.47 |
Run Llama 3.3 70B Instruct on the Framework Desktop, 128GB · or LFM2.5 2.6B
How much use it takes to pay for the machine
Everything above is at 500k tokens a day. Pay-back moves with how much you actually run, so here are both models at the five levels of use the calculator names, on the Framework Desktop, 128GB. LFM2.5 2.6B pays for it sooner at every level of use, so which of them to run does not turn on how hard you work it.
| A day's use | Llama 3.3 70B Instruct | LFM2.5 2.6B |
|---|---|---|
| 50ka few chats a day | 12,740 years | 532 years |
| 200klight assistant use | 3,185 years | 133 years |
| 1Ma moderate coding-assistant day | 637 years | 27 years |
| 4Mheavy coding with an agent | 159 years | 6.7 years |
| 20Magents running most of the day | 116 yearsits ceiling | 16 months |
The Framework Desktop, 128GB generates at most 5.49M tokens a day on Llama 3.3 70B Instruct, so that column's figure at 20M tokens a day is for the most it can do, not for the whole of what was asked.
Run Llama 3.3 70B Instruct at 20M tokens a day · or LFM2.5 2.6B
Machines that run one and not the other
LFM2.5 2.6B needs 2.2 GB of memory at 32k of context and Llama 3.3 70B Instruct needs 53 GB. That puts LFM2.5 2.6B on 24 of the 37 machines priced here that Llama 3.3 70B Instruct does not, starting at $899.
| Machine | Price | Memory | Speed on LFM2.5 2.6B | Pay-back |
|---|---|---|---|---|
| Mac mini M6, 16GB | $899 | 16 GB | 52 tok/s estimated | Pays back in 14 years |
| Mac mini M6, 24GB | $1,099 | 24 GB | 58 tok/s estimated | Pays back in 17 years |
| Strix Halo Framework Desktop, 32GB | $1,269 | 32 GB | 87 tok/s estimated | Pays back in 20 years |
| Mac mini M6, 32GB | $1,299 | 32 GB | 58 tok/s estimated | Pays back in 20 years |
| MacBook Air M5 (13-inch), 16GB | $1,299 | 16 GB | 52 tok/s estimated | Pays back in 20 years |
| AMD Radeon AI PRO R9700, 32GB | $1,299card only | 32 GB | 252 tok/s estimated | Pays back in 20 years |
18 more, on the LFM2.5 2.6B page.
Llama 3.3 70B Instruct is also head to head with DeepSeek-R1-Distill-Llama-70B above it on the leaderboard. LFM2.5 2.6B is also head to head with Devstral Small 2 24B below it on the leaderboard.
The assumptions behind both columns
Both columns use the same usage: 500k tokens a day at 15:1 input to output, 32k of context, $0.17 per kWh, and today's API prices held flat. Speeds marked estimated are worked out from memory bandwidth rather than measured, and pay-back scales with them. Where nobody rents an open model by the token, its API prices are the nearest hosted model's, named beside them. Machines are the 37 here with a published price that are still sold. Change any of it in the calculator.
More head to head: every machine that runs Llama 3.3 70B Instruct · every machine that runs LFM2.5 2.6B · every other match-up · both against the frontier · the quickest pay-back at each level of use