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⇱ RTX PRO 6000 BLACKWELL Local LLM Benchmarks, Context Scaling & Supported Models 2026 – Hardware Corner

Tier 2 Workstation

RTX Pro 6000 Blackwell LLM Performance

Local LLM Performance: 96.9 t/s average on 14B models at 16k context. Updated Benchmarks: March 2026.

Gen (14B 4-bit) 96.9 t/s
PP (14B 4-bit) 5,027 t/s
Max Model 123B
VRAM
96 GB GDDR7
Bandwidth 1,790 GB/s
Token Gen (14B @ 4k Ctx)

96.9T/s

Prompt Proc (14B @ 4k Ctx)

5,027T/s

Summary

After extensive testing, we can call the NVIDIA RTX Pro 6000 Blackwell the undisputed king of local AI performance. Leveraging its massive 96GB of GDDR7 VRAM and blistering bandwidth, we comfortably ran 123B parameter models at Q4 quantization and 120B models with a full 128k context. With support for NVFP4 and Flash Attention 2, our benchmarks confirm it is currently the fastest desktop GPU available for running the large open-source models entirely in memory.

Key Insights

Capable of running Mistral 123B using GGUF (Q4_K), completely offloading the weights in to VRAM.
The fastest dekstop GPU for local LLM
Handles gpt-oss 120B and GLM 4.5 Air with full 128k context
Supports Flash Attention 2, significantly boosting prompt processing speeds.
Supports hardware acceleration for NVFP4 quantization with FP4

Current Price in US

$8,800

Avg. Market Value

Current Pricing

Hardware Specs
VRAM 96GB GDDR7
Capable of running 123B model
Bandwidth 1,790 GB/s
Architecture Blackwell
Memory speed 28 Gbps
Memory bus 512 bit
TDP 600 W
Suggested PSU 1,000 W
Price/GB VRAM $91.67
Price/(t/s) with 14B @ 16k $90.85

Biggest LLMs You Can Run on This GPU

The models below represent the largest language models that fit fully in VRAM on this GPU using 4-bit quantization (GGUF). Benchmarks include token generation and prompt processing speeds measured at their maximum supported context length.

Mistral 123B (Q4_K ) Max 64k
Token Generation 10.5 t/s @ 64k context
Prompt Processing 343.1 t/s @ 64k context
Qwen3.5 122B (Q4_K) Max 256k
Token Generation 65.6 t/s @ 256k context
Prompt Processing 1,013.4 t/s @ 256k context
gpt-oss 120B (MXFP4) Max 128k
Token Generation 99.8 t/s @ 128k context
Prompt Processing 1,289.8 t/s @ 128k context

Note: Context values are grouped into standard tiers (4K, 16K, 32K, 64K, 128K). Models may support slightly higher context, but they remain in the lower tier unless they reach the next bracket.

RTX Pro 6000 Blackwell local LLM Inference Performance vs Similar GPUs

Compare prompt ingestion and token generation speeds against similar GPUs across widely used local models and extended context lengths up to 256K.

Local LLM Benchmarks

Prompt processing (t/s) and token generation speed (t/s) across different open weight models and context lengths.

Prompt Processing
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K ) 10,964.1 7,587.7 5,300.7 1,921.6 1,009.7 —
Qwen3 14B (Q4_K ) 6,865.6 5,027.2 3,536.8 1,378.1 717.0 —
Gemma4 26B (Q4_K) 9,437.2 8,453.8 7,107.6 5,379.6 3,667.8 2,245.7
Qwen3.5 27B (Q4_K) 3,338.5 2,980.5 2,526.7 1,856.7 1,404.9 903.1
Qwen3 30B A3B (Q4_K ) 6,546.0 5,084.5 3,863.0 2,535.6 1,270.3 637.4
Gemma4 31B (Q4_K) 3,749.8 3,061.9 2,086.7 1,423.0 876.8 506.8
Qwen3 32B (Q4_K ) 3,137.2 2,374.0 1,687.1 707.2 330.4 —
Llama 3.3 70B (Q4_K ) 1,689.1 1,355.4 1,008.4 528.2 266.1 —
GLM 4.5 Air 106B (Q4_K ) 2,795.2 2,107.2 1,450.3 698.7 320.2 —
gpt-oss 120B (Q8_0)
llama.cpp (8288)
 6,512.8 5,721.5 5,000.3 3,767.4 2,036.6 —
gpt-oss 120B (MXFP4) 4,578.6 4,060.7 3,368.3 2,360.3 1,289.8 —
Qwen3.5 122B (Q4_K)
llama.cpp (8288)
 3,055.0 2,836.1 2,582.9 2,159.3 1,548.0 1,013.4
Mistral 123B (Q4_K ) 994.2 796.6 600.8 343.1 — —
Token Generation
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K ) 173.7 140.6 111.1 77.9 48.3 —
Qwen3 14B (Q4_K ) 114.4 96.9 80.3 60.4 39.8 —
Gemma4 26B (Q4_K) 196.9 172.7 170.3 161.0 133.2 112.5
Qwen3.5 27B (Q4_K) 60.9 57.5 55.1 51.3 45.1 36.3
Qwen3 30B A3B (Q4_K ) 198.7 129.8 103.3 80.2 56.1 38.4
Gemma4 31B (Q4_K) 61.6 59.6 55.8 52.0 43.6 34.4
Qwen3 32B (Q4_K ) 54.9 45.7 39.9 32.1 23.1 —
Llama 3.3 70B (Q4_K ) 32.0 28.2 25.7 21.7 16.6 —
GLM 4.5 Air 106B (Q4_K ) 101.1 82.9 64.2 30.5 17.7 —
gpt-oss 120B (Q8_0)
llama.cpp (8288)
 221.3 193.2 178.5 155.0 123.3 —
gpt-oss 120B (MXFP4) 210.1 182.2 161.5 133.1 99.8 —
Qwen3.5 122B (Q4_K)
llama.cpp (8288)
 98.4 93.7 91.3 86.6 78.1 65.6
Mistral 123B (Q4_K ) 19.1 17.7 15.9 10.5 — —

Frequently Asked Questions

Common questions about running LLMs on the RTX Pro 6000 Blackwell.