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
HuggingFaceBio
Total parameters
14,822,046,720
Exact file size
9,636,318,080 bytes
Quantization / precision
Q4_K_M
Weight repository
HuggingFaceBio/Carbon-X-GGUF
Original model repository
HuggingFaceBio/Carbon-X
Weight revision
cbb978c02e494bbe4043ed582c532296fc97a31e
Model revision
539ad36790e78a38718c547bcb12c55b8a447c56
Runtime notes
Confirm model architecture and quantization support in your runner.
Recorded release date
2026-10-21
Date evidence
Source repository created 2026-10-21; availability basis, not a proven announcement date. Apache-2.0. DNA/text research specialist, not a clinical diagnostic tool. Requires llama.cpp b11742 or newer for HybridDNATokenizer. Scores come from the pinned GGUF card, not source BF16.
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.
FrontierVEP · Carbon 1000
67.8 / 100
DNA single-base variant classification accuracy on 1000 problems (500 benign, 500 pathogenic). Q4_K_M through llama-server; specialist research benchmark.
Benchmark efficiency: 7.036 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: Quantization-specific publisher evaluation: llama-server, Q4_K_M, thinking disabled, temperature 0.6, top-p 0.95, top-k 20, min-p 0; one sample per problem, 1000 problems.
Publisher result; no independent replication or confidence interval supplied. Apache-2.0. DNA/text research specialist, not a clinical diagnostic tool. Requires llama.cpp b11742 or newer for HybridDNATokenizer. Scores come from the pinned GGUF card, not source BF16. Weight size excludes runtime and KV-cache memory; this is not a measured device-fit or speed result.
DNA next-base accuracy on 600 problems. Q4_K_M through llama-server; not a general language score.
Benchmark efficiency: 4.006 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: Quantization-specific publisher evaluation: llama-server, Q4_K_M, thinking disabled, temperature 0.6, top-p 0.95, top-k 20, min-p 0; one sample per problem, 600 problems; per-base accuracy.
Publisher result; no independent replication or confidence interval supplied. Apache-2.0. DNA/text research specialist, not a clinical diagnostic tool. Requires llama.cpp b11742 or newer for HybridDNATokenizer. Scores come from the pinned GGUF card, not source BF16. Weight size excludes runtime and KV-cache memory; this is not a measured device-fit or speed result.
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 9.636 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 9.636 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.