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URL: https://www.hardware-corner.net/gpu-llm-benchmarks/rtx-6000-ada/

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

RTX 6000 Ada LLM Performance

Local LLM Performance: 58.5 t/s average on 14B models at 16k context. Updated Benchmarks: March 2026.

Gen (14B 4-bit) 58.5 t/s
PP (14B 4-bit) 2,367 t/s
Max Model 70B
VRAM
48 GB GDDR6
Bandwidth 960 GB/s
Token Gen (14B @ 4k Ctx)

58.5T/s

Prompt Proc (14B @ 4k Ctx)

2,367T/s

Summary

In our 2026 testing, we found the RTX 6000 Ada represents an ideal hardware profile for local LLM users, combining familiar RTX 3090-level speeds with a massive 48GB VRAM pool. This extended capacity comfortably unlocks the 70B parameter model tier, allowing us to run Llama 3.3 70B fully offloaded without relying on system RAM. However, the economics for typical users are difficult to justify. At its current price is roughly five times as much as an RTX 3090. While we consider it a highly capable and power-efficient card, its high price-per-GB makes multiple-GPU setups a far more practical choice for budget-conscious local deployments.

Key Insights

Capable of running Llama 3.3 70B using GGUF (Q4_K) completely in VRAM.
Delivers token generation speeds comparable to the RTX 4090 but with double the memory capacity.
Provides 48GB of GDDR6 VRAM with a 960 GB/s bandwidth within a highly efficient 300W TDP.
Carries a steep price premium, costing roughly five times as much as a used RTX 3090 despite only doubling the VRAM.

Current Price in US

$5,000

Avg. Market Value

Current Pricing

Hardware Specs
VRAM 48GB GDDR6
Capable of running 70B model
Bandwidth 960 GB/s
Architecture Ada Lovelace
Memory speed 20 Gbps
Memory bus 384 bit
TDP 300 W
Suggested PSU 700 W
Price/GB VRAM $104.17
Price/(t/s) with 14B @ 16k $85.46

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.

Llama 3.3 70B (Q4_K) Max 16k
Token Generation 13.7 t/s @ 16k context
Prompt Processing 526.0 t/s @ 16k context
Qwen3 32B (Q4_K) Max 64k
Token Generation 11.8 t/s @ 64k context
Prompt Processing 348.6 t/s @ 64k context
Qwen3 30B A3B (Q4_K) Max 128k
Token Generation 20.7 t/s @ 128k context
Prompt Processing 448.6 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 6000 Ada 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) 4,932.5 4,096.4 2,218.5 1,152.6 541.3 —
Qwen3 14B (Q4_K) 2,912.1 2,367.2 1,310.2 727.4 358.6 —
gpt-oss 20B (MXFP4) 6,495.8 5,350.5 3,818.7 2,270.3 1,177.7 —
Qwen3 30B A3B (Q4_K) 3,888.0 3,156.5 2,010.6 913.2 448.6 —
Qwen3 32B (Q4_K) 1,203.9 977.5 595.5 348.6 — —
Llama 3.3 70B (Q4_K) 678.1 526.0 — — — —
Token Generation
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 133.9 98.7 68.9 39.9 20.8 —
Qwen3 14B (Q4_K) 81.2 58.5 42.3 27.0 15.2 —
gpt-oss 20B (MXFP4) 160.8 137.1 116.3 88.4 57.5 —
Qwen3 30B A3B (Q4_K) 199.6 120.1 74.3 40.8 20.7 —
Qwen3 32B (Q4_K) 37.4 25.1 18.2 11.8 — —
Llama 3.3 70B (Q4_K) 18.1 13.7 — — — —

Frequently Asked Questions

Common questions about running LLMs on the RTX 6000 Ada.