XORTRON.CriminalComputing.LARGE.2026.3 — Hardware Requirements & GPU Compatibility
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XORTRON.CriminalComputing.LARGE.2026.3 is a 122.6B-parameter open language model from darkc0de. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 74.60 GB of VRAM — see which GPUs and Macs can run it below.
XORTRON.CriminalComputing.LARGE.2026.3 runs on a 96 GB+ Mac or a workstation GPU
Best local pick → Mac Studio (M3 Ultra, 96GB) · Q4_K_M · 79.8 GB · ~8 tok/s
Macs from 64 GB (Q2_K · ~11 tok/s)
WTFPL
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run XORTRON.CriminalComputing.LARGE.2026.3
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve darkc0de/XORTRON.CriminalComputing.LARGE.2026.3 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX PRO 6000 (96 GB) (16 GB headroom) · Q4_K_M
- Generation speed
- ~16 tok/s
- generation speed
- Cost per 1M output tokens
- $24.61
- per 1M output tokens
How Much VRAM Does XORTRON.CriminalComputing.LARGE.2026.3 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 53.1 GB | 64.2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 58.3 GB | |
| 60.8 GB | 71.9 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 66 GB | |
| 74.6 GB | 85.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 79.8 GB | |
| 88.4 GB | 99.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 93.6 GB | |
| 102.2 GB | 113.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 107.4 GB | |
| 123.7 GB | 134.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 128.8 GB | |
| 246.3 GB | 257.3 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 251.4 GB |
Selected Q4_K_M
4-bit medium quantization79.8GBat 16K context
- Weights
- 73.6 GB
- KV cache
- 5.9 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 55.8 GB more
Which Hardware Can Run XORTRON.CriminalComputing.LARGE.2026.3?
Q4_K_M · 74.6 GBXORTRON.CriminalComputing.LARGE.2026.3 (Q4_K_M) requires 74.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 97+ GB is recommended. Using the full 131K context window can add up to 46.5 GB, bringing total usage to 121.1 GB. No consumer GPU has enough memory. 30 devices with unified memory can run XORTRON.CriminalComputing.LARGE.2026.3, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio (M3 Ultra, 96GB).
64–80 GB
Q4_K_M · best fit Q2_K (58.3 GB)
3 Mac classes · 1 device family
M2 Ultra*64 GB~11Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~8Simulate output on Mac Studio M5 Max (64 GB)Orin~3Simulate output on NVIDIA Jetson AGX Orin 64GB96 GB
fits · 16.2 GB free · up to 32K ctx
2 Mac classes
M3 Ultra*96 GB~8Simulate output on Mac Studio (M3 Ultra, 96GB)M3 Max*96 GB~3Simulate output on MacBook Pro 14" M3 Max (96 GB)
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 122B A10BAlibaba | 109.4 | 75.5 GB | —needs > 24 GB | same size |
| GPT OSS 120BOpenAI | 97.5 | 70.8 GB | —needs > 24 GB | same size |
| NVIDIA Nemotron 3.5 Super VL 120B A12B BF16NVIDIA | — | 87.7 GB | —needs > 24 GB | same size |
| Ling 3.0 Flash VLInclusion AI | — | 76 GB | —needs > 24 GB | same size |
| Leanstral 1.5 119B A6BMistral AI | — | 84 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- darkc0de
- Parameters
- 122.6B
- Architecture
- MistralForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 32,768
- Release Date
- 2026-02-21
- License
- WTFPL
Frequently Asked Questions
How much VRAM does XORTRON.CriminalComputing.LARGE.2026.3 need?
XORTRON.CriminalComputing.LARGE.2026.3 requires 74.6 GB of VRAM at Q4_K_M, or 246.3 GB at BF16. Full 131K context adds up to 46.5 GB (121.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 122.6B × 4.8 bits ÷ 8 = 73.6 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 47.5 GB (at full 131K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run XORTRON.CriminalComputing.LARGE.2026.3?
No — XORTRON.CriminalComputing.LARGE.2026.3 requires at least 53.1 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
What's the best quantization for XORTRON.CriminalComputing.LARGE.2026.3?
For XORTRON.CriminalComputing.LARGE.2026.3, Q4_K_M (74.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (88.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 53.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run XORTRON.CriminalComputing.LARGE.2026.3 on a Mac?
Yes — Mac Pro M2 Ultra (64 GB) and 41 other Macs can run XORTRON.CriminalComputing.LARGE.2026.3, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 74.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio (M3 Ultra, 96GB).
Can I run XORTRON.CriminalComputing.LARGE.2026.3 locally?
Yes — XORTRON.CriminalComputing.LARGE.2026.3 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is XORTRON.CriminalComputing.LARGE.2026.3?
At Q4_K_M, XORTRON.CriminalComputing.LARGE.2026.3 can reach ~64 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 74.6 × 0.65 = ~70 tok/s
Estimated speed at Q4_K_M (74.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of XORTRON.CriminalComputing.LARGE.2026.3?
At Q4_K_M, the download is about 73.57 GB. The full-precision BF16 version is 245.22 GB. The smallest option (Q2_K) is 52.11 GB.
Which GPUs can run XORTRON.CriminalComputing.LARGE.2026.3?
No single consumer GPU has enough VRAM to run XORTRON.CriminalComputing.LARGE.2026.3 at Q4_K_M (74.6 GB). Multi-GPU or professional hardware is required.
Which devices can run XORTRON.CriminalComputing.LARGE.2026.3?
30 devices with unified memory can run XORTRON.CriminalComputing.LARGE.2026.3 at Q4_K_M (74.6 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.