FitMyLLM
NVIDIA· BLACKWELL 2.0

NVIDIA RTX 6000D

Running LLMs on the RTX 6000D — the long read: which models fit at which quantisation, and the settings worth changing. · Or what else $7,500 buys

VRAM
84 GB
FLAGSHIP
BANDWIDTH
1570
GB/S
MODELS Q4
383/449
85%
7B Q4 SPEED
~179
BLAZING
▸ MODEL COVERAGE @ Q485% OF ALL
▸ ESTIMATED SPEED· BY MODEL SIZE @ Q4

Average speeds at Q4 quantization. Actual performance varies by model architecture and context length.

3B
~419
TOK/S
7B
~179
TOK/S
14B
~90
TOK/S
32B
~39
TOK/S
70B
~18
TOK/S
▸ MEASURED RIG REPORTS

We rent the machine and time every model on it: decode, VRAM peak, concurrency, watts and cost per million tokens. Yours may already be one of them — and two are free to read in full.

SEE THE REPORTS
▸ SPECIFICATIONS
VRAM
84 GB
BANDWIDTH
1570 GB/s
FP16 COMPUTE
97 TFLOPS
TDP
600W
MEMORY
GDDR7
ARCHITECTURE
Blackwell 2.0
CUDA CORES
19,968
TENSOR CORES
624
PCIE
Gen 5 x16
MSRP
$7,500
351
FAST MODELS · >30 TOK/S
Real-time chat speed
383
USABLE · >10 TOK/S
Comfortable for all tasks
383
TOTAL COMPATIBLE
Fit in VRAM at Q4
▸ RENT IT IN THE CLOUD

Buying RTX 6000D costs $15–$40k and isn’t practical for most teams. Spin one up by the hour instead:

Spin up in ~60s. Pay by the second. Cancel anytime.

Some links are affiliate links — we may earn a small commission at no extra cost to you. This helps keep FitMyLLM free and independent.

▸ COMPATIBLE MODELS· 383
S
Falcon-H1R Tiny 90M0.09B
FALCON·256K CTX· CHAT· REASONING
13956
TOK/S · 1% VRAM
S
SmolLM2 135M0.135B
SMOLLM·2K CTX· CHAT
9304
TOK/S · 1% VRAM
S
nomic-embed-text-v1.5 100M0.14B
EMBEDDING·8K CTX· CHAT
8971
TOK/S · 1% VRAM
S
GPT-2 124M0.14B
GPT2·1K CTX· CHAT
8971
TOK/S · 1% VRAM
S
SmolVLM 256M0.256B
SMOLLM·8K CTX· CHAT· VISION
4906
TOK/S · 1% VRAM
S
Gemma 3 270M0.27B
GEMMA·32K CTX· CHAT
4652
TOK/S · 1% VRAM
S
Snowflake Arctic Embed M v2.00.305B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
4118
TOK/S · 1% VRAM
S
bge-large-en-v1.5 335M0.335B
EMBEDDING·1K CTX· CHAT
3749
TOK/S · 1% VRAM
S
mxbai-embed-large-v10.335B
EMBEDDING·1K CTX· EMBEDDING
3749
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L0.335B
EMBEDDING·1K CTX· EMBEDDING
3749
TOK/S · 1% VRAM
S
LFM2 350M0.35B
LFM·125K CTX· CHAT· TOOL_USE
3589
TOK/S · 1% VRAM
S
SmolLM2 360M0.36B
SMOLLM·8K CTX· CHAT
3489
TOK/S · 1% VRAM
S
GPT-2 Medium 345M0.38B
GPT2·1K CTX· CHAT
3305
TOK/S · 1% VRAM
S
Qwen 2.5 0.5B0.5B
QWEN·32K CTX· CHAT
2512
TOK/S · 1% VRAM
S
SmolVLM 500M0.5B
SMOLLM·8K CTX· CHAT· VISION
2512
TOK/S · 1% VRAM
S
Falcon-H1 0.5B0.52B
FALCON·128K CTX· CHAT
2415
TOK/S · 1% VRAM
S
BGE-M30.568B
EMBEDDING·8K CTX· EMBEDDING
2211
TOK/S · 1% VRAM
S
Snowflake Arctic Embed L v2.00.568B
EMBEDDING·8K CTX· EMBEDDING· MULTILINGUAL
2211
TOK/S · 1% VRAM
S
Qwen3-Embedding 0.6B0.6B
EMBEDDING·32K CTX· EMBEDDING· MULTILINGUAL
2093
TOK/S · 1% VRAM
S
Falcon-H1R Tiny 0.6B0.6B
FALCON·32K CTX· CHAT· REASONING
2093
TOK/S · 1% VRAM
S
Falcon Perception 0.6B0.6B
FALCON·4K CTX· VISION
2093
TOK/S · 1% VRAM
S
Qwen 1.5 0.5B0.62B
QWEN·32K CTX· CHAT
2026
TOK/S · 1% VRAM
S
LFM2 700M0.74B
LFM·125K CTX· CHAT· TOOL_USE
1697
TOK/S · 1% VRAM
S
Qwen3 0.6B0.75B
QWEN·32K CTX· CHAT· REASONING
1675
TOK/S · 1% VRAM
S
GPT-2 Large 774M0.81B
GPT2·1K CTX· CHAT
1551
TOK/S · 1% VRAM
S
Qwen 3.5 0.8B0.87B
QWEN·256K CTX· CHAT· CODING· MULTILINGUAL
1444
TOK/S · 1% VRAM
S
InternVL3 1B0.94B
OTHER·32K CTX· CHAT· VISION
1336
TOK/S · 1% VRAM
S
MiniCPM5 1B1.08B
MINICPM·128K CTX· CHAT· REASONING· MULTILINGUAL
1163
TOK/S · 1% VRAM
S
TinyLlama 1.1B1.1B
LLAMA·2K CTX· CHAT
1142
TOK/S · 1% VRAM
S
LFM2.5-1.2B-Thinking1.2B
LFM·122K CTX· CHAT· REASONING· TOOL_USE
1047
TOK/S · 1% VRAM
S
Llama-3.2-1B1.2B
LLAMA·4K CTX· CHAT
1047
TOK/S · 1% VRAM
S
LFM2 1.2B1.2B
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
1047
TOK/S · 1% VRAM
S
Zamba2 1.2B1.2B
OTHER·4K CTX· CHAT
1047
TOK/S · 1% VRAM
S
EXAONE-4.0-1.2B1.3B
EXAONE·64K CTX· CHAT
966
TOK/S · 2% VRAM
S
OPT 1.3B1.3B
OPT·2K CTX· CHAT
966
TOK/S · 2% VRAM
S
MiniCPM-V 4.61.3B
OTHER·256K CTX· CHAT· VISION
966
TOK/S · 2% VRAM
S
DeepSeek Coder 1.3B1.35B
DEEPSEEK·16K CTX· CODING
930
TOK/S · 2% VRAM
S
Phi-1 1.3B1.42B
PHI·2K CTX· CODING
885
TOK/S · 2% VRAM
S
Phi-1.5 1.3B1.42B
PHI·2K CTX· CHAT· CODING
885
TOK/S · 2% VRAM
S
LFM2 8B A1B8.3BMoE
LFM·125K CTX· CHAT· TOOL_USE· MULTILINGUAL
837
TOK/S · 7% VRAM
▸ NEXT STEP

Get personalized recommendations.

See ranked models with benchmark scores, run commands, and precise speed estimates for your RTX 6000D.

WHAT THIS CARD IS WORTH

RTX 6000D holds 383 of the models in our catalogue and is, in practice, a IQ4_XS card — the largest it takes is Mistral Medium 3.5 at IQ4_XS.

TOKENS/SEC PER $100
1.9
8B at Q4_K_M, so cards compare like for like
VRAM PER $100
1.12 GB
what memory costs on this card
THE BIGGEST IT TAKES
Mistral Medium 3.5128B · IQ4_XS75.2 GB9 tok/sEST
Pixtral Large 124B124B · IQ4_XS73.0 GB9 tok/sEST
Nemotron 3 Super 120B-A12B123.61B · Q4_K_S75.0 GB90 tok/sEST
Mistral-Large 123B123B · IQ4_XS72.4 GB9 tok/sEST
Devstral 2 123B123B · IQ4_XS72.4 GB9 tok/sEST
Qwen 3.5 122B A10B122B · Q4_K_S73.8 GB108 tok/sEST
Nemotron 3 Super 120B120B · Q4_K_S73.8 GB90 tok/sEST
Mistral Small 4 119B119B · Q4_K_M75.3 GB159 tok/sEST
AGAINST CARDS OF SIMILAR MEMORY
M1 Ultra (128GB)96 GB$4,9991.4 tok/s per $100
M2 Ultra (128GB)96 GB$3,9991.8 tok/s per $100
M4 Max (128GB)96 GB$3,9991.4 tok/s per $100
H100 PCIe 96 GB96 GB$25,0000.1 tok/s per $100
RTX 6000D84 GB$7,5001.9 tok/s per $100

Neighbours in memory rather than in price: memory decides whether a card can do the job at all, so two cards of the same size at different prices is the comparison you are making. Ordered by memory, then bandwidth — not by the value column, which is worked out from bandwidth and price alone and therefore rewards a cheap card whatever its software stack does to that bandwidth in practice. Read it as one input, not as a ranking.

▸ DEVICE UNDER TEST

NVIDIA RTX 6000D — 84 GB VRAM.

RTX 6000D SPEC
BRAND
NVIDIA
VRAM
84 GB GDDR7
BANDWIDTH
1570 GB/s
FP16 COMPUTE
97 TFLOPS
FP32 COMPUTE
97 TFLOPS
CUDA CORES
19,968
TENSOR CORES
624
TDP
600 W
ARCHITECTURE
Blackwell 2.0
MSRP
$7500
▸ AI CAPABILITY
383/ 449 models @ Q4

With 84 GB VRAM and 1570 GB/s bandwidth, this GPU handles models up to 117B parameters.

Speed ≈ bandwidth / model_size × efficiency. A 7B model at Q4 runs at ~179 tok/s.

RENT IT — LIVE PRICES
Checking what the clouds are charging…
§ 01TOP MODELS FOR RTX 6000D
383 FIT · SHOWING 20
MODELSIZEVRAM Q4TOK/SAVG
GPT-OSS 120B117B72.0 GB24654.1
Command A 111B111B68.3 GB1127.6
GLM 4.5 Air110B67.7 GB10551.0
Qwen 1.5 110B110B67.7 GB1133.4
Llama 4 Scout 17B-16E109B67.1 GB7433.9
Cogito v2 109B MoE109B67.1 GB74
Ling 2.6 Flash107.49B66.2 GB17036.8
Sarvam 105B105B64.7 GB1248.0
Command-R+ 104B104B64.1 GB1252.7
Llama-3.2-90B-Vision-Instruct90B55.5 GB1448.5
Hunyuan A13B80B49.4 GB9781.1
Qwen3-Coder-Next80B49.4 GB41943.0
Qwen3-Next 80B A3B80B49.4 GB41949.0
NVLM-D 72B79.38B49.0 GB1648.7
InternVL3 78B78B48.2 GB1680.6
Qwen2.5-72B72.7B44.9 GB1739.7
Qwen2-VL 72B72.7B44.9 GB1755.5
Qwen 1.5 72B72B44.5 GB1749.7
Qwen2 Math 72B72B44.5 GB1749.7
Molmo 72B72B44.5 GB1754.1