Products · Lambda · Workstation

Lambda Vector Pro

Chips: NVIDIA RTX PRO 5000 Blackwell 48GB or NVIDIA RTX PRO 6000 Blackwell or NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
VRAM: 48 or 96 GB
Released: 2025

Product page (opens in a new tab)Discontinued 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 PRO 5000 Blackwell 48GB	48.0 GB	48.0 GB	1,344 GB/s	517 of 622	—
NVIDIA RTX PRO 6000 Blackwell	96.0 GB	96.0 GB	1,792 GB/s	547 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 96.0 GB configuration

Maker	Lambda
Type	Workstation
Chip	NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
Video memory (VRAM)	96.0 GB
Usable for AI models	96.0 GB
Memory bandwidth	1,792 GB/s
FP16 compute	110.0 TFLOPS
Notes	Workstation with NVIDIA RTX professional graphics; each option below is a GPU you can configure. Lambda ended its workstation and server business on Aug 29, 2025; warranties are still supported.
Product page	lambda.ai (opens in a new tab)Discontinued

Best LLMs for the Lambda Vector Pro 96.0 GB

Quality	Model	Quant	Memory	Speed	Runs
93Excellent quality (A+) · 93 of 100: how capable the model is at F16, whatever it runs on. Match score 97/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.6 35B-A3BAlibaba	`F16`	77.5 GB	98.2 tok/s	Runs great
93Excellent quality (A+) · 93 of 100: how capable the model is at F16, whatever it runs on. Match score 96/100 (quality, fit, speed and memory use) sets the ranking.	MiniCPM-V 4.7 35B-A3BOpenBMB	`F16`	76.0 GB	98.2 tok/s	Runs great
93Excellent quality (A+) · 93 of 100: how capable the model is at F16, whatever it runs on. Match score 96/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.5 35B-A3BAlibaba	`F16`	77.5 GB	98.2 tok/s	Runs great
92Excellent quality (A+) · 92 of 100: how capable the model is at F16, whatever it runs on. Match score 96/100 (quality, fit, speed and memory use) sets the ranking.	Nex N2 Mini (35B-A3B)Nex-AGI	`F16`	75.8 GB	98.2 tok/s	Runs great
91Excellent quality (A+) · 91 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 96/100 (quality, fit, speed and memory use) sets the ranking.	Nemotron 3.5 Bolt 74B-A9BNVIDIA	`Q6_K`	66.9 GB	105 tok/s	Runs great
91Excellent quality (A+) · 91 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	KAT-Coder V2.5 Dev (35B-A3B)Kuaishou	`F16`	74.9 GB	98.2 tok/s	Runs great
91Excellent quality (A+) · 91 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	Sage-A1.5 35B-A3BInternScience	`F16`	77.5 GB	98.2 tok/s	Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	Kolibri 1 (78B-A3.5B)Aleph Alpha	`Q6_K`	69.6 GB	170 tok/s	Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	Ornith 1.5 35B-A3BDeepReinforce	`F16`	77.5 GB	98.2 tok/s	Runs great
92Excellent quality (A+) · 92 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	Gemma 4 26B-A4BGoogle	`F16`	57.4 GB	85.2 tok/s	Runs great

Best Image models for the Lambda Vector Pro 96.0 GB

Quality	Model	Quant	Memory	Speed	Runs
91Excellent quality (A+) · 91 of 100: how capable the model is at FP8, whatever it runs on. Match score 96/100 (quality, fit, speed and memory use) sets the ranking.	MiniMax H3 ImageMiniMax	`FP8`	72.0 GB	7.3 s	Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at FP16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	Ming-Image 0.1 DesignAnt Group (inclusionAI)	`FP16`	63.9 GB	13 s	Runs great
89Excellent quality (A+) · 89 of 100: how capable the model is at FP16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	Qwen-Image 2.1 TurboAlibaba	`FP16`	46.7 GB	9.4 s	Runs great
92Excellent quality (A+) · 92 of 100: how capable the model is at FP16, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	Ideogram 4Ideogram	`FP16`	57.3 GB	20 s	Runs well
92Excellent quality (A+) · 92 of 100: how capable the model is at FP16, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	Krea 2 TurboKrea	`FP16`	37.4 GB	3.5 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.	Cosmos3-Super Text2Image (64B)NVIDIA	`FP8`	69.1 GB	29 s	Runs well

Best Video models for the Lambda Vector Pro 96.0 GB

Quality	Model	Quant	Memory	Speed	Runs
87Excellent quality (A+) · 87 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	LTX-2.5 (22B, video + audio)Lightricks	`Q8 GGUF`	49.8 GB	84 s	Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at FP16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking.	LTX-2.3Lightricks	`FP16`	75.4 GB	5.4 min	Runs well
82Great quality (A) · 82 of 100: how capable the model is at FP16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	LTX-2 (19B, video + audio)Lightricks	`FP16`	75.4 GB	5.4 min	Runs well
88Excellent quality (A+) · 88 of 100: how capable the model is at FP8, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	MiniMax H3 (33B, video + audio)MiniMax	`FP8`	66.8 GB	12.6 min	Slow
86Excellent quality (A+) · 86 of 100: how capable the model is at FP16, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.	Wan 2.2 I2V A14BAlibaba	`FP16`	58.0 GB	21.5 min	Slow

Best Decision models for the Lambda Vector Pro 96.0 GB

Quality	Model	Quant	Memory	Speed	Runs
90Excellent quality (A+) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3 Reranker 4BAlibaba	`F16`	13.7 GB	38 ms	Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at F16, whatever it runs on. Match score 95/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3 Reranker 8BAlibaba	`F16`	22.1 GB	73 ms	Runs great
90Excellent quality (A+) · 90 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.	Skywork Reward V2 Llama 3.1 8BSkywork	`Q8_0`	10.4 GB	109 ms	Runs great
88Excellent quality (A+) · 88 of 100: how capable the model is at F16, whatever it runs on. Match score 94/100 (quality, fit, speed and memory use) sets the ranking.	zerank-2ZeroEntropy	`F16`	13.7 GB	38 ms	Runs great
86Excellent quality (A+) · 86 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	GLiDE 27BFastino	`Q8_0`	34.8 GB	96 ms	Runs great
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`	6.8 GB	9.0 ms	Runs great
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.	Shieldstral 1.0 3BMistral AI	`F16`	13.3 GB	36 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.	Intern-Decision 4BShanghai AI Lab	`F16`	14.5 GB	27 ms	Runs great

Other products with the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation EditionNVIDIA · Workstation card · 96 GB · $8,565Product page (opens in a new tab)
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AceleMax Series DL-E400AMAX · Workstation · 24, 32, 48 or 96 GBProduct page (opens in a new tab)
APEXX T4 PRO-XBOXX · Workstation · 4, 8, 16, 24, 32, 48 or 96 GBProduct page (opens in a new tab)
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A+ Workstation AS-2115HV-TNRTSupermicro · Workstation · 20, 48 or 96 GBProduct page (opens in a new tab)
Valence Workstation - VWS-172109266Exxact · Workstation · 4, 8, 20, 24, 32, 40, 48 or 96 GBProduct page (opens in a new tab)
TensorEX Rackmount Workstation - TWS-154715021Exxact · Workstation · 4, 8, 20, 24, 32, 40, 48, 94 or 96 GBProduct page (opens in a new tab)
TensorEX Rackmount Workstation - TWS-123065716Exxact · Workstation · 16, 20, 24, 32, 48, 72 or 96 GBProduct page (opens in a new tab)
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Valence Workstation - VWS-135223847Exxact · Workstation · 20, 24, 32, 48, 72 or 96 GBProduct page (opens in a new tab)
Valence Workstation - VWS-173807702Exxact · Workstation · 4, 8, 20, 24, 32, 40, 48, 72 or 96 GBProduct page (opens in a new tab)

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.