Llama-modchip: 284B DeepSeek on 64GB RAM, no GPU

Break the 64 GB RAM Wall: Run 284B DeepSeek-V4-Flash (< 59 GiB RAM) & 30B MoEs (up to 111 tok/s) at 256K Context on Pure CPU — Zero GPUs Required.

⚡ Quickstart · 🧩 Supported Models · 🖥️ Supported Chips & Platforms · 🏆 Leaderboard · 📄 Preprint PDF (paper/) · 📐 Architecture · ❓ FAQ

284.33B
_{Frontier MoE params}

56.56 GiB
_{checkpoint on disk}

57.61 GiB
_{peak VmRSS @ 256K ctx}

44.36 tok/s
_{284B CPU prefill}

60.4–111.6 tok/s
_{30B MoE CPU decode}

13.1 ms
_{Claude Code restore}

0
_{GPUs required}

Real-time Frontier & Agentic MoE inference on the CPU workstation you already own — 100% in RAM, zero NVMe swap:

the machine you have	RAM	recommended model	speed (0 GPUs)	what is going on
a developer laptop / mini-PC	16–32 GB	Nemotron-3.5-30B-A3B (`14.35 GiB`)	25.0–60.4 tok/s	46 Mamba-2 SSM layers + 1.64 MB L3 Stockfish-PVS drafter; 256K ctx in 6 GiB KV
a 64 GB desktop / server	64 GB	DeepSeek-V4-Flash 284B (`56.56 GiB`)	44.4 tok/s pp · 9.1–14.7 tok/s tg	SwiGLU-H2 shaves 20.31 GiB; entire 284B model + 256K ctx sits in RAM (57.61 GiB VmRSS)
a hybrid desktop (CPU + 16 GB GPU)	32–64 GB	Nemotron-30B / Qwen3-30B	80.80 tok/s	hot attention trunk on GPU (`-ngl 99`), cold routed MoE experts in CPU DDR5
an 8-channel workstation (Threadripper)	128 GB+	DeepSeek-284B / Nemotron-30B	111.62 tok/s (30B)	8-CCD AVX-512BW `_1x8` register tiling saturates 8-channel DDR5 memory bandwidth

TL;DR — The 3 Paradigm-Breaking Pillars of llama-modchip:

🐋 284.33B Frontier MoE in a 64 GB CPU Box: Shrinks DeepSeek-V4-Flash from 76.87 GiB (which crashes in OOM on 64 GB Linux) down to 56.56 GiB on disk / 57.61–58.9 GiB VmRSS at full 256K context (44.36 tok/s prefill, 9.06–10.74 tok/s pure CPU decode, 11.5–14.7 tok/s with PVS) using closed-form Analytical SwiGLU-H2 Second-Order Hessian Compression + Fractal-64 (Q2_0, 3.75 bpw) MLA transcoding + 4-Level Register (ZMM AVX-512BW) + L1D (32 KB) + L2 (1 MB) + L3 (64 MB) Whole-Model Tiling + $L_2$-Quadrature MoE Pruning.

⚡ 30B MoE at GPU Speeds on Pure CPU: Runs NVIDIA-Nemotron-3.5-30B-A3B in 14.35 GiB at 25.0–60.4 tok/s on 16-core Zen 5 (111.6 tok/s on Threadripper, 80.8 tok/s on AMD Vulkan Hybrid, 44.97 tok/s on AWS Graviton3 ARM64) via a 1.64 MB Stockfish-PVS v2 + RouteCrack L2/L3 Drafter (96.4% acceptance) and _MM_HINT_NTA L3 cache isolation (HARPCache).

🤖 Zero-API Claude Code (./modchip claude): Eliminates the 45-second CPU agent prompt re-evaluation bottleneck via /dev/shm Slot-0 KV Warm Restore (18.9 ms on Nemotron-30B / 13.1–32.6 ms on DeepSeek-284B) with all 40 Claude Code tools active.

⚡ Quickstart

🐧 Linux & 🍎 macOS (1-Command Automated Setup & Chat)

git clone https://github.com/synchopate/llama-modchip.git
cd llama-modchip
./modchip chat

⚡ Zero-Compile Direct Run (Pre-Compiled Linux `x86_64` Binary + Hugging Face `.gguf`)

⚠️ Important Compatibility Note (GGML_TYPE_Q2_0 = 40): Our Fractal-64 models use custom block-64 BF16-scale Gray-coded tensors (GGML_TYPE_Q2_0, type ID 40) and custom deepseek4 / Stockfish-PVS kernels. Opening them in unpatched llama.cpp, Ollama, or LM Studio will fail with unknown tensor type 40. Use either ./modchip above or our standalone single-file pre-compiled binary (llama-server-modchip-linux-x86_64, zero .so dependencies) bundled directly inside the Hugging Face repositories and GitHub Release v2.4.0:

# 1. For 30B Nemotron-3.5-A3B (14.35 GiB — runs in 16–32 GB RAM at 25–60.4 tok/s):
hf download Synchopate/NVIDIA-Nemotron-3.5-30B-A3B-Fractal-64-CPU-GGUF \
  llama-server-modchip-linux-x86_64 NVIDIA-Nemotron-3.5-30B-A3B-Fractal-64.gguf --local-dir .
chmod +x llama-server-modchip-linux-x86_64
./llama-server-modchip-linux-x86_64 -m NVIDIA-Nemotron-3.5-30B-A3B-Fractal-64.gguf -c 262144 -t 16 --port 8080

# 2. For 284B DeepSeek-V4-Flash (56.56 GiB — fits in 64 GB RAM at 9.1–14.7 tok/s):
hf download Synchopate/DeepSeek-V4-Flash-0731-Fractal-64GB-CPU-GGUF --local-dir ./dsv4
chmod +x ./dsv4/llama-server-modchip-linux-x86_64
./dsv4/llama-server-modchip-linux-x86_64 -m ./dsv4/DeepSeek-V4-Flash-0731-Fractal-00001-of-00003.gguf -c 32768 -t 16 -fa on --port 8080

🪟 Windows 10/11 (1-Click Hybrid GPU + CPU MoE Pack)

Open windows/ and double-click windows/AVVIA_MODCHIP.bat (or run ./modchip start in Git Bash). It automatically detects your CPU cores, free RAM, and GPU VRAM (nvidia-smi), downloads official llama.cpp CUDA 12.4 / Vulkan binaries, computes the optimal --n-cpu-moe split, and opens the Web Chat UI (http://127.0.0.1:8080).

`./modchip` Command	What It Does
`./modchip chat`	💬 Interactive terminal chat with live `tok/s`, TTFT, and `Stockfish-PVS` acceptance telemetry
`./modchip web`	🌐 Opens the Dark-Mode Web Chat & Live Telemetry HUD (`http://127.0.0.1:8787`)
`./modchip bench`	📊 Runs a 30-second 2-stage speed test and prints a shareable CPU Benchmark Scorecard
`./modchip claude`	🤖 Launches Claude Code CLI (`40 tools`) wired to the local 256K engine (`18.9–32.6 ms` warm restore)
`./modchip eval`	🧪 Runs the reproducible 100-Point Quality, Tool-Calling, Coding & 8.5K Needle Gate
`./modchip quantize`	🗜️ Transcodes `BF16`/`Q4_0`/`IQ2_XXS` GGUFs into `Fractal-64` (`3.75 bpw`) or `Fractal-MXFP4` (`4.25 bpw`)
`./modchip status` / `stop`	🔍 Inspects CPU readiness, `2MB HugePages`, and `/dev/shm` IPC, or stops background services

🏆 Can your CPU run a 284B Frontier MoE in 64 GB RAM? Test in 2 seconds (Zero Downloads):

curl -sL https://raw.githubusercontent.com/synchopate/llama-modchip/main/scripts/check_cpu_readiness.py | python3

👉 Paste your terminal scorecard in a 📊 1-Click GitHub Benchmark Issue or GitHub Discussions to be added to the Official Community CPU Leaderboard!

🧩 Supported Models & Model-Specific Optimizations

Each model family has a dedicated deep-dive optimization guide with exact mathematical proofs, operator-level latency breakdowns, and quality gate benchmarks:

Model Family	Total / Active Params	Disk (`GGUF`) & `256K` RAM (`VmRSS`)	Key Optimizations Implemented	Verified Speed (`16C Zen 5` / Hybrid)	Detailed Guide
🚀 `NVIDIA-Nemotron-3.5-30B-A3B` (`Fractal-64` / `MXFP4` / `Q4_0`)	`30.2B` / `3.0B` (`23 SSM + 23 MoE + 6 GQA`)	`14.35 – 17.59 GiB` (`15.4 – 21.7 GiB` RSS)	• Block-64 `Fractal-64` (`3.75 bpw`) & `MXFP4` (`4.25 bpw`) (`100/100` Quality Gate) • `Stockfish-PVS v2 (M=7)` + `RouteCrack` (`1.64 MB` L2/L3 Zobrist TT) • `432 MB` 256K EAKV (`Q8_0/Q4_0`) + `_MM_HINT_NTA` L3 isolation • `18.9 ms` `/dev/shm` Slot-0 Restore (`~6,800x` faster warm turns)	`25.0 – 60.4 tok/s` (`0 GPU`) `80.8 tok/s` (`Vulkan Hybrid`) `111.6 tok/s` (`Threadripper`)	📖 Nemotron Guide
🐋 `DeepSeek-V4-Flash-0731-Fractal` (`SwiGLU-H2 + Fractal-64`)	`284.33B` / `13.17B` (`43L MLA + 256+1s MoE`)	`56.56 GiB` (`-20.3 GiB`) (`58.9 GiB` RSS* — fits in `64 GB`!)*	• Analytical 2nd-Order `SwiGLU-H2` MoE + `Fractal-64` MLA Transcoding • 4-Level Register (`ZMM` AVX-512BW) + L1D (`32KB`) + L2 (`1MB`) + L3 (`64MB`) Multi-Column (`{8,4,3,2}`) Prefill Tiling (`25.0 -> 44.36 tok/s`, `+77%`) • Register-Stationary AVX-512 `MQA8` FlashAttention (`2.0x`) + AVX-512 `quantize_row_q8_K` • $L_2$-Quadrature MoE Pruning + L1-Local Gate-First SwiGLU (`10.74 tok/s`) • *Exact SVD of `wo_b wo_a`** (`99.82%` CosSim at `-25%` rank) & $O(1)$ Rollback Fix	`43.2–44.4 tok/s` (Prefill) `9.06–9.95 tok/s` (Lossless) `9.87–10.15 tok/s` (`Top-5 L2`) `10.41–10.74 tok/s` (`Top-4+SwiGLU`) `11.5–14.7 tok/s` (`PVS`)	📖 DeepSeek-V4 Guide
⚡ `Qwen3-30B-A3B` & `Qwen3-14B` (`Q3_K_M` / `Q4_K_M`)	`30.5B` / `3.3B -> 1.7B` (`48L GQA`, `128` exp)	`13.70 – 17.28 GiB` (`14.5 – 19.4 GiB` RSS)	• Norm-Preserving Dynamic MoE Top-K (`8 -> 4`, `+38.5%` decode) • 3:1 Interleaved SWA Safety Lock (`LLAMA_SWA_STRIDE=4`) • Windows `11 GB VRAM + 16 GB RAM` Hybrid Split (`--n-cpu-moe N`)	`34.80 tok/s` (`0 GPU` Top-4) `50–58 tok/s` (`14B` RTX 2080 Ti)	📖 Architecture §3.9
🏎️ `NVIDIA-Nemotron-H-8B-128K` (`Q8_0` / `Q4_K_M`)	`8.0B` Dense (`48 SSM + 4 GQA`)	`4.60 – 8.00 GiB` (`210 MB` KV @ `128K`)	• 48-Layer Mamba-2 SSM Constant-Memory Backbone • 100% GPU VRAM Residency on `8–11 GB` Cards (`0 GB` System RAM)	`100+ tok/s` (`RTX 2080 Ti`) `147.6 tok/s` (`CPU pp512`)	📖 Nemotron Guide

🖥️ Supported Chips & Processor-Specific Optimizations

Full microarchitectural details and tuning flags for every supported processor and GPU hybrid are documented in docs/HARDWARE_OPTIMIZATIONS.md:

Processor / Platform	Verified Hardware	Hardware-Specific Optimizations Active	Platform Guide & Scripts
AMD Zen 5 (`Desktop / Mobile / Server`)	`Ryzen 9 9955HX`, `9950X`, `EPYC 9005 Turin`	• Single-cycle 512-bit AVX-512 (`VPDPBUSD`, `VPRORVQ`, `VBMI`, `BF16`) • `48 KB` L1D 3D Tiling (`TILE_I0=64`) on multi-head `wo_a` projections • `_MM_HINT_NTA` Victim-L3 Isolation (`64 MB`) + `2MB` THP (`6.14 GB` L2 STLB)	📖 Hardware Guide §2.1 `install.sh`
AMD Zen 4 Multi-CCD (`Threadripper / EPYC`)	`Threadripper PRO 7985WX` (`64C/128T`, `8 CCDs`, `256 MB` L3)	• Dynamic sysfs L3 CCD Topology Discovery (`scripts/llama_modchip.py`) • Deterministic `4C/CCD` Stride (`-t 32`): avoids Infinity Fabric saturation (`+54%` faster than `-t 64`)	📖 Hardware Guide §2.2 Issue #1
AMD 3D V-Cache + Radeon Vulkan Hybrid	`Ryzen 9 7945HX3D` (`128 MB` L3) + `RX 9070 XT 16GB` (`RADV`)	• Tensor-Role Splitting (`-ngl 99 --n-cpu-moe N --load-mode none`) • Native Vulkan `SSM_SCAN` + `Fractal-MXFP4` (`15.2 GB`) GPU Shaders (`80.8 tok/s` at 256K)	📖 Hardware Guide §2.3 `FAQ.md` Q8
Intel / AMD + NVIDIA RTX (`Windows 10/11`)	`Core i9-9900K / 14900K` + `RTX 2080 Ti 11GB / 3090 / 4090`	• Single-Cycle AVX2 `vpsllvd + vpmaddwd` Sub-Scale Fusion • Live-VRAM `--n-cpu-moe` Solver (`550 MB` WDDM Reserve) + `_WIN32` `CreateFileMappingA`	📖 Windows Guide `windows/modchip_win.ps1`
Apple Silicon (`M1 / M2 / M3 / M4`)	`Apple M4 Pro` (`14C CPU + 20C GPU`, `24 GB` Unified)	• Zero-Copy Unified Memory Metal Shaders + ARM64 NEON/AMX • `0`-Swap `128K` Context Budgeting (`384 MB` KV) + `Stockfish-PVS v2`	📖 Apple Silicon Guide `apple-silicon/`
ARM64 Cloud & Edge (`AWS Graviton / Ampere`)	`AWS Graviton3 m7g.2xlarge` (`8 vCPU Neoverse-V1`, `32 MB` L3)	• ARMv8.4-A DotProd (`SDOT`) + 256-Bit SVE + 1-vCPU-per-core pinning (`-t 8`) • `32 MB` System Level Cache (`SLC`) retention (`44.97 tok/s` decode)	📖 Hardware Guide §1 Issue #2
AMD Zen 4 Compact Mini-PC (`Fedora / Linux`)	`Ryzen 7 8745HS` (`GEEKOM A8`, `8C/16T`, `16 MB` L3 + `780M`)	• AVX-512 VNNI + `16 MB` L3 window auto-tuning (`LLAMA_KV_L3_WINDOW=1024`) • Optional `Radeon 780M` UMA iGPU offload	📖 Fedora Guide `fedora/nemotron.sh`

📊 Headline Benchmarks: Pure CPU & Hybrid Summary

1. `DeepSeek-V4-Flash 284B` (`13.17B` Active) on a `64 GB` Zen 5 CPU (`AMD Ryzen 9 9955HX`, `0 GPUs`)

Full operator-level profiling and mathematical proofs in docs/DEEPSEEK_V4_OPTIMIZATIONS.md:

`DeepSeek-V4-Flash-0731-Fractal` (`56.56 GiB` @ `256K` Context)	Prefill (`pp512`)	Pure Decode (`N=1`)	With `Stockfish-PVS`	Prob Cosine Sim	KL-Divergence ($\Delta$ PPL)	Top-1 / Top-3 Match
Standard `IQ2_XXS` (`76.87 GiB`, Unpatched)	🔴 OOM on 64GB	`5.91 tok/s` (`169.3 ms`)	N/A (Rollback Bug)	`1.00000`	`0.0000` (`0.00%`)	`100% / 100%`
1. `v2.4.0` Lossless (`Top-6` All Layers, Default)	`43.16 – 44.36 tok/s` (`+77.1%`)	`9.06 – 9.95 tok/s`	`11.5 – 14.7 tok/s`	`1.00000`	`0.0000` (`0.00%` Bit-Exact)	`100% / 100%`
2. `v2.4.0` Sweet-Spot (`Top-5` on `il >= 3`, $L_2$ Quadrature)	`43.16 – 44.36 tok/s`	`9.68 – 9.87 tok/s`	`12.2 – 15.1 tok/s`	`0.99714`	`0.0042 nats` (`+0.42%` PPL)	`95.8% / 100%`
3. `v2.4.0` `Top-5 L2` + Gate-First SwiGLU (`80/128` $L_2$)	`43.16 – 44.36 tok/s`	`10.15 tok/s` (`98.5 ms`)	`12.8 – 15.6 tok/s`	`0.99385`	`0.0531 nats`	`100% / 100%`
4. `v2.4.0` Turbo `10+` (`Top-4 L2` + Gate-First SwiGLU)	`43.16 – 44.36 tok/s`	`10.12 – 10.74 tok/s` (`93.2 ms`)	`13.0 – 16.0 tok/s`	`0.99126`	`0.0281 – 0.0694 nats`	`100% / 100%`

2. `NVIDIA-Nemotron-3.5-30B-A3B` (`3.0B` Active) at `256K` Context (`AMD Ryzen 9 9955HX`, `0 GPUs`)

Full ablation tables and 100/100 Quality Gate report in docs/NEMOTRON_OPTIMIZATIONS.md:

Quantization Profile	Disk Size	Idle `VmRSS` (`256K`)	Quality Gate (`100` pts)	Prefill (`pp512`)	Base Decode (`N=1`)	`Stockfish-PVS v2 (M=7)` Refactoring
`Baseline-Q4_0` (Official GGUF)	`17.59 GiB`	`21.41 GiB`	`100.0 / 100.0`	`241.0 – 306.8 tok/s`	`19.54 tok/s`	`40.91 avg / 45.43 peak tok/s`
`Fractal-MXFP4` (`4.25 bpw`)	`15.95 GiB`	`17.46 GiB`	`100.0 / 100.0`	`241.0 – 306.8 tok/s`	`26.08 tok/s` (`+33.5%`)	`48.39 avg / 60.40 peak tok/s` (`80.8` Vulkan)
`Fractal-64-BF16` (`3.75 bpw`)	`14.35 GiB`	`15.40 GiB` (`-6.01 GiB`)	`100.0 / 100.0`	`241.0 – 306.8 tok/s`	`25.02 tok/s` (`+28.0%`)	`48.39 avg / 60.40 peak tok/s` (`96.4%` acc)

🏆 Community CPU & Hybrid Leaderboard

Run ./modchip bench (or windows\BENCHMARK_PC.bat on Windows) and ➕ Submit Your Scorecard (1-Click GitHub Issue)!

Hardware Platform (`CPU / SoC / Hybrid`)	Cores / Cache	Memory	Prefill (`tok/s`)	Decode — Prose / C++20	Decode — `Stockfish M=7` Burst	Config & Contributor
AMD Threadripper PRO 7985WX (`Zen 4`)	`64C/128T` (`8 CCDs`, `256 MB` L3)	`256 GB` 8-Ch DDR5-6000 (`0 GPU`)	`386.00` (`-t 32`)	`73.84 – 80.14 tok/s` (`70.0%` acc)	`111.62 tok/s` warm / `80.14` cold (`-t 32` 4C/CCD)	`@aifartist` (Issue #1)
AMD Ryzen 9 7945HX3D + RX 9070 XT (`Hybrid`)	`16C/32T` (`128 MB` 3D V-Cache) + `16 GB` Vulkan	`94 GB` DDR5 + `16 GB` VRAM	`563.00` (256K ctx)	`75.10 tok/s` (`MXFP4` 15.2GB)	`80.80 tok/s` (Vulkan `SSM_SCAN` + `MXFP4` MoE)	`Community (RADV/Vulkan)` (`FAQ.md`)
AMD Ryzen 9 9955HX / 9950X (`Zen 5`)	`16C/32T` (`2 CCDs`, `64 MB` L3)	`64 GB` DDR5 (`0 GPU`)	`241.0` (Peak `306.8`)	`25.02 – 34.5 tok/s` (`Fractal-64` / `MXFP4`)	`48.39 avg / 60.40 peak` (`len 6.90`)	`@synchopate` (`docs/NEMOTRON_OPTIMIZATIONS.md`)
Apple M4 Pro (`14-Core SoC`)	`14C CPU + 20C Metal`	`24 GB` Unified (`0 Swap`)	`256.0 – 549.0`	`47.0 – 48.0 tok/s` (128K)	`49.0+ tok/s`	`@jarorosFork` (`apple-silicon/`)
AWS Graviton3 `m7g.2xlarge` (`ARM64 Neoverse-V1`)	`8 vCPU` (`32 MB` L3, `SVE-256`)	`31 GB` DDR5 (`0 GPU`)	`101.67` (`-t 8`)	`41.05 tok/s` (C++20)	`44.97 tok/s` (Refactoring)	`@gerchik` (Issue #2)
Windows 11 Hybrid (`i9-9900K + RTX 2080 Ti 11GB`)	`8C/16T` (`16 MB` L3) + `11 GB` CUDA	`16 GB` DDR4 + `11 GB` GDDR6	Auto CUDA 12.4	`50–58 tok/s` (`14B`) / 30B Hybrid	`100+ tok/s` (`Nemotron-H-8B` 128K)	`windows/`
AMD Ryzen 7 8745HS (`GEEKOM A8`)	`8C/16T` (`16 MB` L3)	`64 GB` DDR5-5600	Verified Fedora 44	Verified (`ngram`)	Verified (`CPU` & `780M`)	`@jarorosFork` (`fedora/`)
🐋 `DeepSeek-V4-Flash 284B` on Ryzen 9 9955HX	`16C/32T` (`2 CCDs`, `64 MB` L3)	`64 GB` DDR5 (`58.9 GiB` RSS @ `256K`)	`43.16 – 44.36 tok/s` (`32.6 ms` Slot-0)	`9.06 – 10.74 tok/s` (`13.17B` active)	`11.50 – 14.69 tok/s` (`94.5%` PVS acc)	`@synchopate` (`docs/DEEPSEEK_V4_OPTIMIZATIONS.md`)

🛠️ Click to expand: Repository Structure & Manual CMake Build Instructions

llama-modchip/
├── modchip                                    # Unified CLI (setup | start | chat | bench | eval | quantize | web | claude | stop)
├── install.sh                                 # 1-Command Automated Installer (Linux)
├── README.md / ARCHITECTURE.md / FAQ.md       # Clean Overview, Full Mathematical Paper & Engineering FAQ
├── RELEASE_NOTES.md                           # v2.4.0 Release Notes
├── docs/
│   ├── NEMOTRON_OPTIMIZATIONS.md              # Deep-dive on Nemotron-3.5-30B-A3B & Nemotron-H-8B optimizations
│   ├── DEEPSEEK_V4_OPTIMIZATIONS.md           # Deep-dive on DeepSeek-V4-Flash 284B profiling, SVD & L2-quadrature MoE
│   └── HARDWARE_OPTIMIZATIONS.md              # Chip-by-chip optimizations (Zen 5, Threadripper, Vulkan, CUDA, Metal, ARM64)
├── patches/
│   ├── deepseek-v4-flash-fractal-256k.patch   # Unified v2.4.0 patch (DeepSeek-V4-Flash 284B + Nemotron-3.5 30B, Linux/macOS/Windows)
│   └── zen5-nemotron-256k-optimizations.patch # Standalone ggml/llama core patch for Nemotron-3.5
├── windows/                                   # 1-Click Windows 10/11 Hybrid GPU+CPU MoE Pack (AVVIA_MODCHIP.bat, modchip_win.ps1)
├── apple-silicon/                             # Apple M4 Pro (24 GB) Metal setup & 128K benchmarks
├── fedora/                                    # Fedora 44 / AMD Ryzen 7 8745HS (GEEKOM A8) all-in-one script
├── speculative/                               # RouteCrack + Stockfish-PVS v2 (rc_stockfish.h, ngram-map.cpp, 96+ techniques)
├── benchmarks/                                # eval_quality_and_tools.py (100-pt Quality Gate) & results/*.json
├── gateway/sse_gateway.py                     # Agentic SSE Proxy + Compact Tool Table + Dark-Mode Web Chat HUD (:8787)
└── scripts/                                   # build_fractal_moe_gguf.sh, transcode_dsv4.{c,py}, chat.py, llama_modchip.py

🏢 Enterprise & Hardware Sponsorships

llama-modchip demonstrates that modern multi-core CPUs and compact Mini-PCs with 64–128 GB of DDR5 RAM can run 284B Frontier MoEs (DeepSeek-V4-Flash) and 30B Hybrid MoEs (Nemotron-3.5-30B-A3B) locally at full 256K context—eliminating $30,000+ multi-GPU hardware requirements for air-gapped enterprise code intelligence, security auditing, and autonomous agent fleets.

We actively partner with hardware manufacturers, cloud silicon teams, and AI research labs to build and validate architecture-specific kernel patches:

🖥️ CPU & Workstation OEMs (AMD Ryzen AI / Threadripper / EPYC, Minisforum, Framework, System76, Apple, Intel): Sponsor a physical test unit or workstation to receive dedicated L1D/L2/L3 cache-tiling & SIMD kernels and permanent verification on the Official README CPU Leaderboard.
☁️ Cloud & ARM64 Silicon Teams (AWS Graviton4, AmpereOne, Google Axion, Azure Cobalt): Sponsor cloud compute credits to validate SVE2 / SME / multi-socket NUMA kernels.
🤝 Enterprise Support & Custom Model Transcoding: Reach out via GitHub Issues or GitHub Sponsors to sponsor custom SwiGLU-H2 + Fractal-64 model transcoding and on-premise deployment.