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All estimates are approximations based on mathematical models and public specifications. Actual performance may vary. Do not make purchasing decisions based solely on these estimates.

Data sourced from Hugging Face, Ollama, and official model documentation. Model names and logos are trademarks of their respective owners.

© 2026 Will It Run AI — Fase Consulting Ibiza, S.L. (NIF: B57969656)

Home/Hardware/GPUs/RTX A6000 48GB
NVIDIA

NVIDIA

RTX A6000 48GB

RTX AWorkstationAmperePCIe 4CUDA
48GB
VRAM
768GB/s
Bandwidth
77TFLOPS
FP16 Compute
1.2kTOPS
INT8 Inference
$4,650 MSRP
VRAM48 GBBandwidth768 GB/sCompute77 TFInference1.2k TOPSValue1.66 TF/$k
RTX A6000 48GBCategory AvgNVIDIA A16 64GB

Specifications

Compute
FP1677 TFLOPS
INT81232 TOPS
ArchitectureAmpere
Memory
VRAM48 GB
Bandwidth768 GB/s
General
FamilyRTX A
SegmentWorkstation
InterconnectPCIe 4
Compute PlatformCUDA
MSRP$4,650

Architecture

Ampere

Ampere is NVIDIA's second-generation RTX architecture, built on Samsung's 8nm process. It introduced 3rd-generation Tensor Cores with support for sparsity-accelerated INT8 operations and improved FP16 throughput over Turing.

AI Relevance

Sparsity-aware Tensor Cores can effectively double throughput for structured sparse workloads. However, the lack of FP8 support means quantized inference is less efficient than Ada Lovelace or Blackwell.

Process: Samsung 8nmPlatform: CUDATensor Cores: Gen 3Precisions: FP32, FP16, BF16, INT8, INT4

Recommendations by Workload

Agentic Coding

C

Devstral Small 2 24B Instruct

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 39.9 tok/s · 55K ctx · llama.cpp
27.8 GB / 48.0 GB VRAM

Chat

C

Qwen 3 32B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 29.9 tok/s · 14K ctx · llama.cpp
27.7 GB / 48.0 GB VRAM

Coding

C

Devstral Small 2 24B Instruct

This model is a direct match for coding. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 39.9 tok/s · 32K ctx · llama.cpp
24.1 GB / 48.0 GB VRAM

RAG

C

Command R 35B

This model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 27.3 tok/s · 40K ctx · llama.cpp
38.0 GB / 48.0 GB VRAM

Reasoning

C

Qwen 3 32B

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 29.9 tok/s · 25K ctx · llama.cpp
30.2 GB / 48.0 GB VRAM

Full Model Compatibility

AlibabaQwen3-Coder 30B A3B Instruct
C53
30.5B25.1 GB81 tok/s31K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
C53
30B24.8 GB84 tok/s31K ctx
moe
UnslothQwen3.5 35B A3B
C53
35B32.5 GB27 tok/s24K ctx
dense
Lmstudio-communityLQwen3.5 35B A3B
C53
35B32.5 GB27 tok/s24K ctx
dense
AlibabaQwen 2.5 Coder 32B
C52
32B30.2 GB30 tok/s25K ctx
dense
UnslothQwen3.5 27B
C51
27B26.4 GB35 tok/s29K ctx
dense
Unslothgemma 3 27b it
C51
27B26.4 GB35 tok/s29K ctx
dense
MistralDevstral Small 2 24B Instruct
C50
24B24.1 GB40 tok/s32K ctx
dense
MistralDevstral Small 1.1
C50
24B24.1 GB40 tok/s32K ctx
dense
MistralCodestral 2 25.08
C50
22B22.6 GB44 tok/s34K ctx
dense
UnslothQwen3.5 9B
C48
9B12.6 GB106 tok/s61K ctx
dense
HauhauCSHQwen3.5 9B Uncensored HauhauCS Aggressive
C48
9B12.6 GB106 tok/s61K ctx
dense
Lmstudio-communityLQwen3.5 9B
C48
9B12.6 GB106 tok/s61K ctx
dense
BartowskiBMeta Llama 3.1 8B Instruct
C47
8B11.8 GB120 tok/s65K ctx
dense
XtunerXllava llama 3 8b v1 1
C47
8B11.8 GB120 tok/s65K ctx
dense
UnslothDeepSeek R1 0528 Qwen3 8B
C47
8B11.8 GB120 tok/s65K ctx
dense
MaziyarPanahiMMeta Llama 3 8B Instruct
C47
8B11.8 GB120 tok/s65K ctx
dense
TheBlokeTLlama 2 7B Chat
C47
7B11.1 GB137 tok/s69K ctx
dense
TheBlokeTMistral 7B Instruct v0.2
C47
7B11.1 GB137 tok/s69K ctx
dense
MaziyarPanahiMMistral 7B Instruct v0.3
C47
7B11.1 GB137 tok/s69K ctx
dense
UnslothQwen3.5 4B
C46
4B8.9 GB239 tok/s86K ctx
dense
BartowskiBLlama 3.2 3B Instruct
C46
3B8.7 GB276 tok/s89K ctx
dense
Lmstudio-communityLgemma 3 4b it
C46
4B8.9 GB239 tok/s86K ctx
dense
QwenQwen2.5 3B Instruct
C46
3B8.3 GB319 tok/s92K ctx
dense
BartowskiBgemma 2 2b it
C46
2B8.1 GB374 tok/s94K ctx
dense
Googlegemma 2b
C46
2B7.7 GB478 tok/s99K ctx
dense
TheDrummerTGemmasutra Mini 2B v1
C46
2B7.7 GB478 tok/s99K ctx
dense
QwenQwen2.5 1.5B Instruct
C45
1.5B7.4 GB584 tok/s104K ctx
dense
Hugging-quantsHLlama 3.2 1B Instruct Q8 0
C45
1B7.3 GB613 tok/s105K ctx
dense
TheBlokeTTinyLlama 1.1B Chat v1.0
C45
1.1B7.2 GB584 tok/s107K ctx
dense
Ggml-orgGSmolVLM 500M Instruct
C45
0.5B6.9 GB613 tok/s111K ctx
dense
Ggml-orgGembeddinggemma 300M
C45
0.3B6.7 GB613 tok/s114K ctx
dense
AlibabaQwen3-Coder-Next
C41
80B55.3 GB32 tok/s14K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B420.8 GB4 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B99.9 GB8 tok/s8K ctx
dense
Z.aiGLM-5
F0
744B465.8 GB4 tok/s4K ctx
moe
Moonshot AIKimi K2.5
F0
1000B620.7 GB3 tok/s4K ctx
moe
MistralMistral Large 3
F0
675B423.9 GB4 tok/s4K ctx
+1moe
MistralMistral Small 4 119B
F0
119B79.3 GB23 tok/s10K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B304.0 GB6 tok/s4K ctx
moe
UnslothQwen3.5 122B A10B
F0
122B84.5 GB9 tok/s9K ctx
dense
DeepSeekDeepSeek V3 671B
F0
671B420.8 GB4 tok/s4K ctx
moe
MistralMixtral 8x22B
F0
141B97.8 GB13 tok/s8K ctx
moe
AlibabaQwen 2.5 72B
F0
72B60.9 GB13 tok/s13K ctx
dense
AlibabaQwen 3 235B A22B
F0
235B152.5 GB11 tok/s5K ctx
moe
UnslothQwen3.5 397B A17B
F0
397B309.9 GB2 tok/s4K ctx
dense
MetaLlama 3.3 70B
F0
70B59.3 GB14 tok/s13K ctx
dense
MetaLlama 4 Maverick 17B 128E
F0
400B252.4 GB7 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B90.8 GB9 tok/s8K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B60.9 GB13 tok/s13K ctx
dense

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

DeepSeekDeepSeek R1 671B
671BTier 5Needs ~426.6 GB
MistralDevstral 2 123B Instruct
123BTier 5Needs ~119.2 GB
Runs on Mac Studio M3 Ultra 256GB
Z.aiGLM-5
744BTier 5Needs ~472.0 GB
Moonshot AIKimi K2.5
1000BTier 5Needs ~625.7 GB
MistralMistral Large 3
675BTier 5Needs ~430.3 GB

Upgrade paths

Upgrade from RTX A6000 48GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

NVIDIANVIDIA A16 64GBNext step up
64 GB VRAM (+16)
B
Unlocks Qwen 2.5 72B, Llama 3.3 70B, Qwen 2.5 VL 72B+8 more

 

NVIDIANVIDIA A800 80GBNVIDIA upgrade
80 GB VRAM (+32)1935 GB/s (+1167)
A
Unlocks Mistral Small 4 119B, Qwen 2.5 72B, Llama 3.3 70B+10 more · +149% faster avg

 

AppleMacBook Pro M3 Max 128GBBest value
128 GB Unified (+80)
B
Unlocks Mistral Small 4 119B, Qwen 2.5 72B, Llama 3.3 70B+12 more

~$2,499 MSRP

AMDAMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+240)8000 GB/s (+7232)
A
Unlocks Devstral 2 123B Instruct, Mistral Small 4 119B, Qwen3-Coder 480B A35B Instruct+26 more · +816% faster avg

~$8,000 MSRP

Compare this GPU