The LPDDR5 memory is listed with a 16GB maximum; no RAM upgrade is assumed.
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.
Fixed 24GB memory and internal storage limit model size; no CUDA support; Amazon main-offer price and stock were not verified in this run.
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.
Memory is soldered and shared with the operating system.
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.
Weight size is not tested compatibility or speed. Check runtime support and memory for context before buying. Prices are dated references. As an Amazon Associate I earn from qualifying purchases.
How these options are selected
Up to three platforms, with the lowest dated reference price on each. Unknown prices follow known prices. Each memory pool keeps a reserve: 30% (minimum 2 GB) for system or unified memory; 20% (minimum 1 GB) for dedicated VRAM. System RAM is for CPU inference. This check does not establish GPU allocation, software support or context capacity.
Model file and details
Provider
webAI
Total parameters
8,791,592,960
Exact file size
5,347,917,568 bytes
Quantization / precision
Q4_K_M
Weight repository
webAI-Official/TwIL-8B
Original model repository
webAI-Official/TwIL-8B
Weight revision
58d49f0d9909e62f9c2fb09e99fc5c76f3f2d630
Model revision
58d49f0d9909e62f9c2fb09e99fc5c76f3f2d630
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-10-14
Date evidence
HF source repository creation date, not a verified public launch date. Current balanced artifact and model card reviewed on 6 November 2026.
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.
GPQA Diamond
70.2 / 100
Hard science questions. This is a science reasoning test, not an overall intelligence score.
Benchmark efficiency: 13.127 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: Publisher internal high-effort pass@1 with thinking enabled, balanced variant. Full MATH-500 and single-year AIME sets; GPQA explicitly means Diamond. IFEval strictness is not stated.
Publisher-reported source-checkpoint result; not an independent test or a score measured on this Q4_K_M file. No confidence interval is supplied. Download bytes cover language weights, not total runtime memory. English reasoning model; 131,072-token context. webAI Non-Commercial License v1.0. Balanced and verbose use different checkpoints: scores must not be transferred between them. Exact decoding settings, harness revision and repeated-run counts are not disclosed.
Following precise instructions. These cards report IFEval without consistently specifying prompt-level versus instruction-level scoring. Treat this view as approximate.
Benchmark efficiency: 17.166 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: Publisher internal high-effort pass@1 with thinking enabled, balanced variant. Full MATH-500 and single-year AIME sets; GPQA explicitly means Diamond. IFEval strictness is not stated.
Publisher-reported source-checkpoint result; not an independent test or a score measured on this Q4_K_M file. No confidence interval is supplied. Download bytes cover language weights, not total runtime memory. English reasoning model; 131,072-token context. webAI Non-Commercial License v1.0. Balanced and verbose use different checkpoints: scores must not be transferred between them. Exact decoding settings, harness revision and repeated-run counts are not disclosed.
Competition math from AIME 2025. Published answer accuracy.
Benchmark efficiency: 16.511 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: Publisher internal high-effort pass@1 with thinking enabled, balanced variant. Full MATH-500 and single-year AIME sets; GPQA explicitly means Diamond. IFEval strictness is not stated.
Publisher-reported source-checkpoint result; not an independent test or a score measured on this Q4_K_M file. No confidence interval is supplied. Download bytes cover language weights, not total runtime memory. English reasoning model; 131,072-token context. webAI Non-Commercial License v1.0. Balanced and verbose use different checkpoints: scores must not be transferred between them. Exact decoding settings, harness revision and repeated-run counts are not disclosed.
Competition math from AIME 2026. Published answer accuracy.
Benchmark efficiency: 16.511 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: Publisher internal high-effort pass@1 with thinking enabled, balanced variant. Full MATH-500 and single-year AIME sets; GPQA explicitly means Diamond. IFEval strictness is not stated.
Publisher-reported source-checkpoint result; not an independent test or a score measured on this Q4_K_M file. No confidence interval is supplied. Download bytes cover language weights, not total runtime memory. English reasoning model; 131,072-token context. webAI Non-Commercial License v1.0. Balanced and verbose use different checkpoints: scores must not be transferred between them. Exact decoding settings, harness revision and repeated-run counts are not disclosed.
Benchmark efficiency: 12.753 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: Publisher internal high-effort pass@1 with thinking enabled, balanced variant. Full MATH-500 and single-year AIME sets; GPQA explicitly means Diamond. IFEval strictness is not stated.
Publisher-reported source-checkpoint result; not an independent test or a score measured on this Q4_K_M file. No confidence interval is supplied. Download bytes cover language weights, not total runtime memory. English reasoning model; 131,072-token context. webAI Non-Commercial License v1.0. Balanced and verbose use different checkpoints: scores must not be transferred between them. Exact decoding settings, harness revision and repeated-run counts are not disclosed.
Math reasoning over all 500 problems. Published exact answer accuracy.
Benchmark efficiency: 18.362 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: Publisher internal high-effort pass@1 with thinking enabled, balanced variant. Full MATH-500 and single-year AIME sets; GPQA explicitly means Diamond. IFEval strictness is not stated.
Publisher-reported source-checkpoint result; not an independent test or a score measured on this Q4_K_M file. No confidence interval is supplied. Download bytes cover language weights, not total runtime memory. English reasoning model; 131,072-token context. webAI Non-Commercial License v1.0. Balanced and verbose use different checkpoints: scores must not be transferred between them. Exact decoding settings, harness revision and repeated-run counts are not disclosed.
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 5.348 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.
Confirm model architecture and quantization support in your runner. The selected weights use 5.348 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.