QuadroProfessionalAmperePCIe 4CUDA
Operating mode
Choose the operating mode for this hardware
Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
About this GPU for AI
The RTX A5500 is a professional Ampere GPU offering 24 GB of ECC GDDR6 at 768 GB/s bandwidth, positioned between the RTX A5000 and RTX A6000 in NVIDIA's Ampere workstation lineup. With 68 TFLOPS FP16 it delivers strong compute for a 24 GB card and suits professional workstations handling 30B quantized inference alongside demanding visualization workflows. At $3,200 it asks a premium over both the consumer RTX 3090 and the RTX A5000 for added compute headroom.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
workstation-gradeecc-memoryprofessional-certifiedmid-workstation
Specifications
Compute
FP1668 TFLOPS
INT8272 TOPS
ArchitectureAmpere
Memory
VRAM24 GB
Bandwidth768 GB/s
General
FamilyQuadro
SegmentProfessional
InterconnectPCIe 4
Compute PlatformCUDA
MSRP$3,200
Key Features
24 GB ECC GDDR6 VRAMAmpere 3rd-gen Tensor Cores68 TFLOPS FP16 / 272 INT8 TOPS768 GB/s memory bandwidthISV-certified professional driversPCIe 4.0 x16 interface
For AI Workloads
Strengths
- 24 GB ECC VRAM runs 13B models at FP16 and 30B models at Q4 with reliability guarantees
- 68 TFLOPS FP16 provides solid compute for a 24 GB Ampere workstation card
- Professional certified drivers and ECC memory suit production AI deployment in enterprise workstations
- 768 GB/s bandwidth provides good decode throughput for 13B and 30B inference
Considerations
- Ampere Tensor Cores lack FP8 — less efficient per watt than Ada workstation cards for quantized inference
- $3,200 is expensive relative to consumer Ampere and Ada cards with similar or better AI capability
- 70B models require very aggressive quantization and will be slow due to 24 GB ceiling
- Ada workstation replacements now available at comparable prices with substantially better efficiency
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
Qwen 3 14B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 59.2 tok/s · 60K ctx · llama.cppEST.
Codestral 2 25.08 is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 42.9 tok/s · 48K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
397BTier 100Needs ~246.9 GB
123BTier 100Needs ~81.0 GB
1000BTier 100Needs ~617.0 GB
1000BTier 100Needs ~617.0 GB
1600BTier 100Needs ~866.2 GB
Image & Video Generation
Diffusion Model Compatibility
41 of 52 models can generate images or video on your RTX A5500 24GB
Upgrade paths
Upgrade from RTX A5500 24GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
MacBook Pro M4 Max 36GBNext step up
36 GB Unified (+12)
AUnlocks 1 additional models that do not fit on the current setup.Unlocks Gemma 4 31B
Unlocks 1 additional models that do not fit on the current setup.
~$2,499 MSRP
32 GB VRAM (+8)
AUnlocks 6 additional models that do not fit on the current setup.Unlocks Gemma 4 31B, Kimi Linear 48B A3B, Falcon 40B Instruct+3 more
Unlocks 6 additional models that do not fit on the current setup.
~$4,000 MSRP
Mac mini M4 64GBBest value
64 GB Unified (+40)
BUnlocks 17 additional models that do not fit on the current setup.Unlocks Qwen 2.5 VL 72B, Gemma 4 31B, Llama 3.3 70B+14 more
Unlocks 17 additional models that do not fit on the current setup.
~$1,099 MSRP
AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+264)8000 GB/s (+7232)
BUnlocks 45 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, Devstral 2 123B Instruct, Qwen 3.5 122B A10B+42 more · +126% faster avg
Unlocks 45 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 126%.
~$8,000 MSRP
Frequently Asked Questions
24
GB
RTX A5500 24GBCategory AvgMacBook Pro M4 Max 36GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~4.7s per image |
| Image Gen (Flux) | Runs with offload | Flux.1 Dev FP16 | ~~21.1s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~25.8s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~4.1s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~12s/frame |
Qwen 3.6 27B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 29.9 tok/s · 69K ctx · llama.cppEST.
Qwen 3 14B matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 75.8 tok/s · 80K ctx · llama.cppEST.
Granite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.
Decode 103.1 tok/s · 93K ctx · llama.cppEST.
21B18.6 GB115 tok/s52K ctx
Image
Image models estimated at 1024×1024 (28 steps, FP16). Video models estimated at 768×512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Buying advice
Should you buy RTX A5500 24GB for local AI?
Excellent choice for local AI
Runs 26 of 50 top models well — a strong all-rounder for local inference.
What will limit you first
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best upgrade itinerary
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Unlocks 1 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M4 Max 36GB (36.0 GB unified memory) is the next step up.
