Qwen3.8 27B Humanlike Chat 2.0 has 26.896 billion total parameters. The selected Q4_K_M GGUF file is 16.559 GB.
Data snapshot: . 1 published test in this catalog.
Model file and details
Provider
LessThanThreeAI
Total parameters
26,895,998,464
Exact file size
16,559,196,608 bytes
Quantization / precision
Q4_K_M
Weight repository
LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF
Original model repository
LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-2.0
Weight revision
469ff8f61f6b27d00ec9c30e80dff5778a519672
Model revision
0a4d369895bf0a8b217fb08ef738769bac9d881b
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-10-02
Date evidence
Humanlike Chat 2.0 Q4_K_M upload and release-card commits dated 2 October 2026; the 8 October repository revision added an optional BF16 vision projector while leaving the selected language-weight file unchanged.
Choose the exact file before buying more memory. The table shows file sizes and the space left after subtracting those sizes from an example memory pool. These are planning estimates, not measured runtime requirements.
Space left still needs to cover the context cache and runtime. System RAM and unified memory must also cover the operating system and other apps. Scroll to compare all three pools.
Formula: remaining GiB = pool GiB − file bytes ÷ 1,073,741,824. GB uses 1,000,000,000 bytes. Check the usable pool and units in your runner; a computer’s advertised memory is not all available to the model. Publisher file records, checked .
16 GiB: IQ4_XS leaves only 1.94 GiB and Q4_K_M leaves 0.58 GiB before runtime overhead. Consider a smaller model if other apps also need this pool.
32 GiB: Q6_K leaves 11.43 GiB; Q8_0 leaves 5.37 GiB. Check context length, parallel requests and backend support before choosing either file.
Speed evidence: the publisher’s GGUF speed figures use one H100 NVL, one request and a 256-token decode. They do not predict mini-PC speed. The IFEval score below uses the publisher’s BF16 reference, not a separate test of each file. Read the test settings and quantization checks.
Compare hardware before buying
These catalog configurations are starting points for comparison. Humanlike Chat 2.0 has not been tested here on either computer. Confirm your runner, usable memory and acceptable speed first.
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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.
IFEval · prompt strict
83.5 / 100
Following every instruction in a prompt. Strict prompt-level accuracy on the full 541-prompt set.
Benchmark efficiency: 5.043 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-reported IFEval strict prompt accuracy on the full 541-prompt set for Humanlike Chat 2.0, using greedy decoding with thinking disabled in the same run as its stated base comparison.
The card states its benchmark reference is the BF16 base-plus-adapter setup; the selected Q4_K_M was not independently benchmarked. The publisher-run result remains unchanged. Training was text only; the 8 October revision adds an optional 931,145,888-byte BF16 vision projector copied from the abliterated base, not evidence of vision-specific fine-tuning or a visual benchmark.
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 16.559 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.
Need a computer for Qwen3.8 27B Humanlike Chat 2.0?
These complete configurations pass the initial weight-size check for the selected 16.56 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 16.559 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.