RigPerDollar

What GPU do you need to run Mistral Small 4 119B?

A new Framework Desktop ($6,799). No priced GPU card holds Mistral Small 4 119B (about 74.1 GB at Q4); this system holds it with room for its full 256K context, at about 47 tok/s estimate. 6 of the 157 GPUs and systems we track fit it.

Parameters119.4B6.5B active (mixture-of-experts)
Q4_K_M GGUF72.64 GBsource
VRAM needed~74.1 GBfile + headroom
Single GPUs that fit6
ReleasedJan 23, 2026source
LicenseApache 2.0
Context window256K tokens

119.4B parameters, 6.5B active (mixture-of-experts) (Hugging Face, accessed 2026-10-18). Q4_K_M GGUF: 72.64 GB (source). Architecture config (Hugging Face config.json): 36 attention layers caching one 320-wide compressed latent (MLA), so about 23 KB of FP16 KV cache per token of context (how we estimate context).

Card: 119B total, 6.5B active, 256K context, MLA attention. The mistral4 architecture is in stock llama.cpp. Fit ignores the 0.87 GB vision projector. Release date is the Hugging Face repo creation date.

Other places to get it

Checked Nov 14, 2026. We list only copies we opened and checked. Check file hashes for copies from other uploaders.

Single GPUs that fit, cheapest first

Single GPU that fitsVRAMSpeed estimateMax context estimatePrice
Framework Desktop, AMD Ryzen AI Max+ PRO 495 (Strix Halo), 192 GB192 GB~47 tok/s256K (full)low $6,799new · source, 2026-10-16
NVIDIA DGX Spark (128GB)128 GB~55 tok/s256K (full)low $6,949.99new · source, 2026-10-23
NVIDIA A100 80GB PCIe80 GB~249 tok/s256K (full)No price yet
NVIDIA RTX PRO 6000 Blackwell Max-Q96 GB~238 tok/s256K (full)No price yet
NVIDIA RTX PRO 6000 Blackwell Server Edition96 GB~223 tok/s256K (full)No price yet
NVIDIA RTX PRO 6000 Blackwell Workstation Edition96 GB~238 tok/s256K (full)No price yet

Every device that runs it

Graphics cards, Macs and mini PCs from the wider hardware list behind My rig, smallest usable memory first. Not sure about yours? Check your exact rig.

Show all 19 devices that run it
DeviceTypeMemorySpeed estimateMax context estimatePrice
NVIDIA A100 80GB PCIeData center GPU80 GB~249 tok/s256K (full)No price yet
Apple M1 Ultra 128GBMac128 GB (~96 usable)~94 tok/s256K (full)No price yet
Apple M2 Ultra 128GBMac128 GB (~96 usable)~94 tok/s256K (full)No price yet
Apple M3 Max (40-core GPU) 128GBMac128 GB (~96 usable)~62 tok/s256K (full)No price yet
Apple M4 Max (40-core GPU) 128GBMac128 GB (~96 usable)~76 tok/s256K (full)No price yet
Apple M5 Max (40-core GPU) 128GBMac128 GB (~96 usable)~81 tok/s256K (full)No price yet
NVIDIA RTX PRO 6000 Blackwell Max-QWorkstation GPU96 GB~238 tok/s256K (full)No price yet
NVIDIA RTX PRO 6000 Blackwell Server EditionData center GPU96 GB~223 tok/s256K (full)No price yet
NVIDIA RTX PRO 6000 Blackwell Workstation EditionWorkstation GPU96 GB~238 tok/s256K (full)No price yet
AMD Ryzen AI Max 385 (128GB)Mini PC128 GB~45 tok/s256K (full)No price yet
AMD Ryzen AI Max 390 (128GB)Mini PC128 GB~45 tok/s256K (full)No price yet
AMD Ryzen AI Max+ 395 (128GB)Mini PC128 GB~45 tok/s256K (full)low $3,499.99new · source, 2026-11-13
NVIDIA DGX Spark (128GB)Mini PC128 GB~55 tok/s256K (full)low $6,949.99new · source, 2026-10-23
NVIDIA RTX Spark N1X laptop (6144-core GPU, 128GB)Laptop128 GB~60 tok/s256K (full)No price yet
Apple M2 Ultra 192GBMac192 GB (~144 usable)~94 tok/s256K (full)No price yet
Apple M3 Ultra 256GBMac256 GB (~192 usable)~95 tok/s256K (full)No price yet
Apple M5 Ultra 256GBMac256 GB (~192 usable)~114 tok/s256K (full)No price yet
Framework Desktop, AMD Ryzen AI Max+ PRO 495 (Strix Halo), 192 GBMini PC192 GB~47 tok/s256K (full)low $6,799new · source, 2026-10-16
Apple M5 Ultra 512GBMac512 GB (~384 usable)~114 tok/s256K (full)No price yet

Multi-GPU: cards that need two or more

CardsTotal VRAMSpeed estimateMax context estimate
4 × AMD Radeon RX 7900 XTX96 GB~129 tok/s256K (full)
4 × NVIDIA GeForce RTX 309096 GB~154 tok/s256K (full)
4 × NVIDIA GeForce RTX 409096 GB~162 tok/s256K (full)
3 × NVIDIA GeForce RTX 509096 GB~238 tok/s256K (full)
2 × NVIDIA RTX A600096 GB~132 tok/s256K (full)

Speed calculator

estimate

Estimate = bandwidth ÷ GB read per token (the model's listed GGUF file: Q4_K_M for most models, otherwise the quant named in the list; scaled by active/total parameters for MoE), scaled by a per-platform efficiency and fixed per-token costs calibrated to 45 measured llama.cpp benchmarks (2025–26). Fit = file size + 1.5 GB headroom ≤ total VRAM (on Macs, the part of unified memory macOS lets the GPU use by default), and room for at least 4K tokens of context with an 8-bit KV cache. For a model with no KV cache data, only the file and headroom are checked, so a fit says nothing about context. Multi-GPU: VRAM adds, speed stays one card's (a layer split runs the cards in turn); max context uses the total. Speed is for short context. Max context = what is left after the file and a 1 GB runtime reserve ÷ KV cache bytes per token, default FP16 and 8-bit (q8_0) cache. How speed works · how context works