Exxact Valence Workstation - VWS-173807702

Products · · Workstation

Exxact Valence Workstation - VWS-173807702

Chips
15 GPU options, from NVIDIA RTX A400 to NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
VRAM
4, 8, 20, 24, 32, 40, 48, 72 or 96 GB
Released
2025

Product page (opens in a new tab) Full AI report

Configurations

Pick one to see which models it runs. More memory lets larger models fit; bandwidth sets how fast they answer.

Configuration Memory Usable for AI Bandwidth LLMs that run well Launch price
NVIDIA RTX A400 4.0 GB 4.0 GB 96 GB/s 215 of 622 —
NVIDIA T1000 8GB 8.0 GB 8.0 GB 160 GB/s 338 of 622 —
NVIDIA RTX A1000 8.0 GB 8.0 GB 192 GB/s 338 of 622 —
NVIDIA RTX 4000 SFF Ada Generation 20.0 GB 20.0 GB 280 GB/s 461 of 622 —
NVIDIA RTX 4000 Ada Generation 20.0 GB 20.0 GB 360 GB/s 461 of 622 —
NVIDIA RTX PRO 4000 Blackwell SFF Edition 24.0 GB 24.0 GB 432 GB/s 466 of 622 —
NVIDIA RTX PRO 4000 Blackwell 24.0 GB 24.0 GB 672 GB/s 466 of 622 —
NVIDIA RTX 4500 Ada Generation 24.0 GB 24.0 GB 432 GB/s 466 of 622 —
NVIDIA RTX PRO 4500 Blackwell 32.0 GB 32.0 GB 896 GB/s 492 of 622 —
NVIDIA RTX 5000 Ada Generation 32.0 GB 32.0 GB 576 GB/s 489 of 622 —
NVIDIA A800 40GB Active 40.0 GB 40.0 GB 1,555 GB/s 511 of 622 —
NVIDIA RTX PRO 5000 Blackwell 48GB 48.0 GB 48.0 GB 1,344 GB/s 517 of 622 —
NVIDIA RTX 6000 Ada Generation 48.0 GB 48.0 GB 960 GB/s 517 of 622 —
NVIDIA RTX PRO 5000 Blackwell 72GB 72.0 GB 72.0 GB 1,344 GB/s 528 of 622 —
NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96.0 GB 96.0 GB 1,792 GB/s 547 of 622 —

Specifications 20.0 GB configuration

MakerExxact
TypeWorkstation
ChipNVIDIA RTX 4000 SFF Ada Generation
Video memory (VRAM)20.0 GB
Usable for AI models20.0 GB
Memory bandwidth280 GB/s
FP16 compute19.2 TFLOPS
NotesWorkstation with NVIDIA RTX professional graphics; each option below is a GPU you can configure. Listed on the NVIDIA Marketplace.
Product pagewww.exxactcorp.com (opens in a new tab)

Best LLMs for the Exxact Valence Workstation - VWS-173807702 20.0 GB

Quality Model Quant Memory Speed Runs
87Excellent quality (A+) · 87 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Gemma 4 26B-A4BGoogle Q4_K_M 17.7 GB 69.3 tok/s Runs welltight fit
83Great quality (A) · 83 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Raon-1 mini Preview (30B-A4B)KRAFTON Q4_K_M 19.2 GB 63.0 tok/s Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.6 35B-A3BAlibaba Q3_K_M 19.4 GB 100 tok/s Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. MiniCPM-V 4.7 35B-A3BOpenBMB Q3_K_M 19.0 GB 100 tok/s Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.5 35B-A3BAlibaba Q3_K_M 19.4 GB 100 tok/s Runs welltight fit
77Great quality (A) · 77 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Nex N2 Mini (35B-A3B)Nex-AGI Q3_K_M 19.0 GB 100 tok/s Runs welltight fit
76Great quality (A) · 76 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. KAT-Coder V2.5 Dev (35B-A3B)Kuaishou Q3_K_M 18.8 GB 100 tok/s Runs welltight fit
76Great quality (A) · 76 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Sage-A1.5 35B-A3BInternScience Q3_K_M 19.4 GB 100 tok/s Runs welltight fit
90Excellent quality (A+) · 90 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Qwen3.8 27BAlibaba Q4_K_M 19.1 GB 12.4 tok/s Runs welltight fit
75Great quality (A) · 75 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking. Ornith 1.5 35B-A3BDeepReinforce Q3_K_M 19.4 GB 100 tok/s Runs welltight fit

Best Image models for the Exxact Valence Workstation - VWS-173807702 20.0 GB

Quality Model Quant Memory Speed Runs
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. ERNIE-Image TurboBaidu FP8 14.5 GB 8.6 s Runs great
83Great quality (A) · 83 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Z-Image TurboAlibaba (Tongyi) Q8 GGUF 13.0 GB 8.6 s Runs great
89Excellent quality (A+) · 89 of 100: how capable the model is at FP8, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. FLUX 3 Image KleinBlack Forest Labs FP8 12.4 GB 15 s Runs well
78Great quality (A) · 78 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 9BBlack Forest Labs Q4 GGUF 16.2 GB 6.5 s Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at FP8, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking. Krea 2 TurboKrea FP8 15.7 GB 12 s Runs greatencoder in RAM
77Great quality (A) · 77 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking. FLUX.2 [klein] 4BBlack Forest Labs Q8 GGUF 10.5 GB 3.4 s Runs great

Best Video models for the Exxact Valence Workstation - VWS-173807702 20.0 GB

Quality Model Quant Memory Speed Runs
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. LTX-Video 2BLightricks FP8 10.8 GB 67 s Runs great
74Great quality (A) · 74 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. HY-OmniWeaving (8.3B, unified video)Tencent FP8 15.7 GB 24.9 min Slowencoder in RAM
81Great quality (A) · 81 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. LTX-2.5 (22B, video + audio)Lightricks Q4 GGUF 16.8 GB 7.1 min Runs wellCPU offload
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5BZhipu AI FP8 14.3 GB 8.1 min Runs well
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. CogVideoX 5B I2VZhipu AI FP8 14.9 GB 8.1 min Runs well

Best Decision models for the Exxact Valence Workstation - VWS-173807702 20.0 GB

Quality Model Quant Memory Speed Runs
86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3.5Jina AI F16 3.0 GB 34 ms Runs great
84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking. Jina Reranker v3Jina AI F16 3.0 GB 34 ms Runs great
83Great quality (A) · 83 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Intern-Decision 4BShanghai AI Lab Q6_K 5.4 GB 107 ms Runs great
82Great quality (A) · 82 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. d1 3BLiquid AI Q8_0 4.9 GB 80 ms Runs great
82Great quality (A) · 82 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. mxbai-rerank-large v2Mixedbread F16 4.7 GB 80 ms Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. Qwen3 Reranker 4BAlibaba Q8_0 6.2 GB 176 ms Runs well
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. FRIDA-DecisionsSber AI F16 2.4 GB 23 ms Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking. GLiNER2.5 DecideFastino F16 1.7 GB 15 ms Runs great

Other products with the NVIDIA RTX 4000 SFF Ada Generation

Spec sources

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.

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