webAI TwIL-LM3-Pro has 3.66 billion total parameters. The selected Q4_K_M GGUF file is 2.244 GB.
Data snapshot: . 1 published test in this catalog.
Model file and details
Provider
webAI
Total parameters
3,659,737,600
Exact file size
2,244,011,744 bytes
Quantization / precision
Q4_K_M
Weight repository
webAI-Official/TwIL-LM3-Pro
Original model repository
webAI-Official/TwIL-LM3-Pro
Weight revision
144a3ccb31264deadcf68bc5a1d58f621e497d03
Model revision
144a3ccb31264deadcf68bc5a1d58f621e497d03
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-09-29
Date evidence
The repository was created on 3 September 2026 under an earlier Meridian identity. webAI renamed it to TwIL-LM3-Pro and finalized the model identity on 29 September 2026; the pinned benchmark card was finalized on 30 September 2026. On 9 October 2026, webAI replaced the selected Q4_K_M artifact with a new build at revision 144a3ccb.
These scores were compiled from the linked sources. These published scores were not independently tested here. Different prompts, precision, thinking budgets and tools can change results.
MATH-500
74.67 / 100
Math reasoning over all 500 problems. Published exact answer accuracy.
Benchmark efficiency: 33.275 published score points per GB of selected weights. This ratio is not an inference-speed measure; check the test settings to see whether the score covers the selected quantization.
Test settings: webAI publisher evaluation of the BF16 checkpoint on 300 MATH-500 examples, with greedy decoding, max_gen_toks 4,096, max_model_len 8,192, repetition penalty 1.0, the chat template with thinking disabled and a vLLM backend.
Publisher-run source-checkpoint result, not an independent evaluation and not a measurement of the selected Q4_K_M file. The card reports that 14.0% of MATH-500 rows hit the generation-length cap and says GGUF scores were not rerun. The replacement 9 October GGUF build was also not benchmarked in that card. The model is specialized for formal logic, is not a general assistant and uses the webAI Non-Commercial License ver. 1.0. The 2.244 GB file excludes runtime, KV cache and context memory; publisher guidance mentioning 4 GB VRAM is not a universal compatibility or speed guarantee.
Context and version evidence: The repository and file name identify the selected model. Revision hashes are shown above when recorded. Tested context length, full hardware configuration and run date are not consistently recorded in this snapshot. Only settings explicitly stated in each benchmark record are known.
The 2.244 GB figure covers model weights in this file. It excludes the context cache, runtime, operating system and any separate multimodal files. It is not a measured RAM requirement or a token-generation speed.
These complete configurations pass the initial weight-size check for the selected 2.24 GB file. Allow extra memory for context and software. Check the model’s runtime notes and your inference software before buying.
Listing evidence: 2026-09-25. Amazon.com base price visible in an owner-supplied screenshot reviewed 25 Sep 2026; capture time not supplied. Dated reference, not a live quote. Excludes card signup offers, coupons, tax and shipping.
Listing evidence: 2026-10-03. Apple US launch price announced 22 September 2026: USD 1,699. Dated manufacturer reference, not an Amazon offer; tax and shipping may differ.
Listing evidence: 2026-09-24. GMKtec, EVO-X2 / 64GB / 1TB, recorded 22 Sep 2026; exact ASIN capacity identified 24 Sep. This is a dated reference, not a current Amazon price; tax, shipping and bundle contents may differ.
Memory matches are estimates, not tested compatibility or speed. As an Amazon Associate I earn from qualifying purchases.
Confirm model architecture and quantization support in your runner. The selected weights use 2.244 GB; this excludes context cache, runtime buffers and other programs.
These are the first listed tiers that pass our reserve policy, not measured minimum requirements. System RAM and VRAM remain separate. Use the memory tool to change the reserve for your context and runner.