VRAM
Memory on the graphics card; usually the main limit for local AI model size.
Component overview
PNY NVIDIA RTX PRO 5000 72GB Blackwell Small Box
PNY graphics card
CategorygpuBrandPNY
Technical specifications
Brand
PNY
Verified manufacturer part number
VCNRTXPRO5000-72-SB
Release
2025 Q1
VRAM
72GB GDDR7 ECC
Architecture
Blackwell
Display Power
300W
Connector Standard
1x PCIe CEM5 16-pin
Minimum PSU
700W
Dual GPU Capable
No
Memory Bus
384-bit
Bandwidth
1344 GB/s
CUDA Cores
14080
Tensor Cores
440
RT Cores
110
Base / Boost Clock
1740 / 2377 MHz
TDP
300W
PCIe Generation
PCIe 5.0
Slot Width
2-slot
Length
267mm
Power Connectors
1x PCIe CEM5 16-pin
Recommended PSU
700W
FP32
65 TFLOPS
AI Score
99
Source
https://www.pny.com/en-eu/nvidia-rtx-pro-5000-72gb-blackwell
Inference Notes
Exact PNY Small Box SKU VCNRTXPRO5000-72-SB with 72GB ECC in one GPU. Two independent Estonian retailer offers were verified; Small Box accessories, current stock, and price are rechecked before quote.
70B-class target requires validation
Selected 70B-class Q4 models need high VRAM or Apple unified memory, short-to-moderate context, and runtime validation.
70B-class fit is still context and runtime sensitive; validate the exact model, quantization, backend, and prompt length before relying on it.
AI terms in plain language
System RAM vs GPU memory
RAM helps apps and data work; GPU memory usually decides which model size can run quickly.
CUDA
NVIDIA’s software layer for many AI tools. Macs and AMD GPUs do not run CUDA workflows the same way.
Apple unified memory
Apple Silicon memory shared by CPU, GPU, macOS, and apps. Useful for Mac AI, but not the same as NVIDIA VRAM.
7B / 8B / 14B / 70B
Approximate model size in billions of parameters. Larger numbers usually need more memory and may run slower.
Quantized models
Compressed models, such as Q4, that use less memory with possible quality or speed tradeoffs.
Context length
How much text the model can keep in mind at once. Longer context uses more memory.
Inference
Running an existing model for chat, coding help, summaries, or document workflows.
Fine-tuning vs adapter tuning
Fine-tuning adapts a model; adapter tuning, such as LoRA/QLoRA, is a lighter way to steer an existing model with examples.
Dual-GPU limitations
Two GPUs do not automatically combine VRAM into one large pool. Software must explicitly support multiple GPUs.
eGPU limitations
External GPUs need enclosure, driver, and runtime validation, and usually do not mean macOS graphics or gaming acceleration.
Planning estimate / written quote
The displayed estimate helps with budgeting. A written quote contains the exact price, confirmed availability, and any proposed substitutions.
Estimated Market Pricing
Estonian market planning estimate. A written quote includes the exact price and confirmed availability.
Low€9,423High€9,423Avg€9,4231 data point
Swipe the chart sideways to inspect every date.
Pricing & Purchase
Planning estimate: €10,836
The displayed price is a planning estimate. A written quote provides the exact price and availability.
Online payment is unavailable for this product; request a quote instead.
Use the quote request on this page. It does not collect payment or card details; we email the exact price and availability.
What happens after your quote request
- The quote request does not collect payment or card details.
- We confirm the exact component, model, and your main requirements.
- We check current Estonian pricing and availability.
- We flag known compatibility constraints based on the details you provide.
- We send the exact price and any alternatives in a written quote.
Support and questions continue through the order or quote email thread.
Request a quote
The request does not collect payment or card details. We send the exact price, availability, and any proposed substitutions in a written quote.
Local AI examples
Examples for PNY NVIDIA RTX PRO 5000 72GB Blackwell Small Box, based on GPU VRAM or Apple unified memory plus RAM headroom. System RAM is not treated as VRAM.
Local AI examples
Examples for PNY NVIDIA RTX PRO 5000 72GB Blackwell Small Box, based on GPU VRAM or Apple unified memory plus RAM headroom. System RAM is not treated as VRAM.
Good fit for private chatGood fit for coding helpGood fit for document summaries70B-class models need strict caveats
Recommended model
Llama 3.3 70B Instruct
A clear upper-limit example for checking whether a machine can attempt a 70B-class model.
It is a boundary test, not the default recommendation for ordinary desktop use.
Comfortable GPU fit
Usable
ollama run llama3.3:70b
Likely good memory headroom for this quantized model at normal context sizes.
- 128GB system/unified memory available
- 72GB effective accelerator memory for model weights and cache
Qwen3.6 35B-A3B
Coding agents, repository analysis, and complex local assistant workflows
A capable MoE model that gives 32GB and 48GB workstations a meaningfully heavier coding target.
Expected experience: Usable
Likely good memory headroom for this quantized model at normal context sizes.
- 128GB system/unified memory available
- 72GB effective accelerator memory for model weights and cache
Gemma 4 12B
Private assistant chat, document understanding, and image or audio analysis
A current multimodal everyday model that makes good use of a 16GB GPU.
Expected experience: Fast
Likely good memory headroom for this quantized model at normal context sizes.
- 128GB system/unified memory available
- 72GB effective accelerator memory for model weights and cache
Qwen3.6 27B
Serious coding help, complex reasoning, and longer document analysis
A strong dense model for 24GB to 32GB GPUs and higher-memory Apple systems.
Expected experience: Fast
Likely good memory headroom for this quantized model at normal context sizes.
- 128GB system/unified memory available
- 72GB effective accelerator memory for model weights and cache
Expandable technical details
Assumptions
- GPU VRAM assumption: 72GB from PNY NVIDIA RTX PRO 5000 72GB Blackwell Small Box.
- Assumed RAM: 128GB.
- Ratings include model weights, estimated KV cache, runtime overhead, and safety margin for one local model running at a time. Treat them as fit guidance, not a speed guarantee.
- GPU product pages assume a sensible amount of system RAM for this VRAM class. Complete build pages show page-specific RAM fit.
Llama 3.3 70B Instruct technical details
Family: Meta Llama 3.3
Parameters: 70.60B
Structure: 70.6B dense
License: Llama 3.3 Community License; allowed with terms; gated access. For guidance only; review the model licence before commercial use.
Native context: 131,072 tokens
Quantization: Q4_K_M
Approx. Q4 weights: 42.5 GB
Default estimate: 51 GB @ 4,096 tokens
Weights / KV / runtime / margin: 42.5 GB / 3 GB / 1.5 GB / 4 GB
CPU/RAM fallback: Not recommended
VRAM: 48 GB minimum / 64 GB recommended
RAM: 64 GB minimum / 96 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Usable
Full GPU offload: Often limited
Context warning: The Q4 weights alone use about 42.5GB before KV cache, runtime overhead, and desktop headroom.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 42.5 GB | 3 GB | 1.5 GB | 4 GB | 51 GB |
| 8K | 42.5 GB | 6 GB | 1.5 GB | 4 GB | 54 GB |
| 16K | 42.5 GB | 12 GB | 1.5 GB | 4.5 GB | 60.5 GB |
| 32K | 42.5 GB | 24 GB | 1.5 GB | 5.5 GB | 73.5 GB |
Qwen3.6 35B-A3B technical details
Family: Qwen3.6
Parameters: 35B
Structure: 35B total / 3B active MoE
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Extended context: 1,010,000 via YaRN; not a default fit assumption
Quantization: Q4_K_M
Approx. Q4 weights: 24 GB
Default estimate: 31.5 GB @ 8,192 tokens
Weights / KV / runtime / margin: 24 GB / 2.5 GB / 2 GB / 3 GB
CPU/RAM fallback: Not recommended
VRAM: 32 GB minimum / 48 GB recommended
RAM: 64 GB minimum / 64 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Usable
Full GPU offload: Only when the memory estimate and context fit
Context warning: Long repository context can use the remaining headroom quickly, especially on 32GB GPUs.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 24 GB | 1.5 GB | 2 GB | 3 GB | 30.5 GB |
| 8K | 24 GB | 2.5 GB | 2 GB | 3 GB | 31.5 GB |
| 16K | 24 GB | 5 GB | 2 GB | 3.5 GB | 34.5 GB |
| 32K | 24 GB | 10 GB | 2 GB | 4 GB | 40 GB |
Gemma 4 12B technical details
Family: Google Gemma 4
Parameters: 11.95B
Structure: 11.95B dense
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Quantization: Q4_0 QAT
Approx. Q4 weights: 7.2 GB
Default estimate: 11.5 GB @ 8,192 tokens
Weights / KV / runtime / margin: 7.2 GB / 1.5 GB / 1.2 GB / 1.5 GB
CPU/RAM fallback: Not recommended
VRAM: 12 GB minimum / 16 GB recommended
RAM: 24 GB minimum / 32 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Fast
Full GPU offload: Only when the memory estimate and context fit
Context warning: Its large context window still requires substantial KV-cache headroom.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 7.2 GB | 1 GB | 1.2 GB | 1.5 GB | 11 GB |
| 8K | 7.2 GB | 1.5 GB | 1.2 GB | 1.5 GB | 11.5 GB |
| 16K | 7.2 GB | 2.5 GB | 1.2 GB | 1.5 GB | 12.5 GB |
| 32K | 7.2 GB | 5 GB | 1.2 GB | 2 GB | 15.5 GB |
Research sources
Researched: 2026-07-30
Qwen3.6 27B technical details
Family: Qwen3.6
Parameters: 27B
Structure: 27B dense
License: Apache License 2.0; allowed. For guidance only; review the model licence before commercial use.
Native context: 262,144 tokens
Extended context: 1,010,000 via YaRN; not a default fit assumption
Quantization: Q4_K_M
Approx. Q4 weights: 17 GB
Default estimate: 23 GB @ 8,192 tokens
Weights / KV / runtime / margin: 17 GB / 2 GB / 1.5 GB / 2.5 GB
CPU/RAM fallback: Not recommended
VRAM: 24 GB minimum / 32 GB recommended
RAM: 48 GB minimum / 64 GB recommended
Current run mode: Comfortable GPU fit
Expected experience: Fast
Full GPU offload: Only when the memory estimate and context fit
Context warning: Repository-scale or very long document context can push a 24GB card beyond a comfortable fit.
| Context | Weights | KV | Runtime | Margin | Estimated GPU memory |
|---|---|---|---|---|---|
| 4K | 17 GB | 1 GB | 1.5 GB | 2.5 GB | 22 GB |
| 8K | 17 GB | 2 GB | 1.5 GB | 2.5 GB | 23 GB |
| 16K | 17 GB | 4 GB | 1.5 GB | 3 GB | 25.5 GB |
| 32K | 17 GB | 8 GB | 1.5 GB | 3.5 GB | 30 GB |
Local AI performance is approximate. Results depend on quantization, context length, backend, drivers, and whether the model plus KV cache fits in VRAM or Apple unified memory.
Trust and process
What happens after a component order or quote request
Exact item and availability
We confirm the exact model, current price, and availability before fulfillment. Submitting a quote request does not collect payment.
Compatibility scope
We review known fit information and flag evident size, power, or interface constraints. Final compatibility depends on the rest of your system and the details you provide.
Handover in Estonia
Pickup or local delivery method and timing are agreed after availability is confirmed.
Returns, warranty, and support
Return and warranty handling depends on order state, the component, manufacturer, and retailer. Questions continue through the order or quote email thread.
Trust details
Important before ordering
Contact and support
Questions continue through the order or quote email thread. Replying to the confirmation is the fastest path.
Warranty
Warranty handling depends on the component, manufacturer, and retailer; the practical path is confirmed case by case.
Exact item
The manufacturer, model, availability, and final amount are confirmed in checkout or in writing before fulfillment. A replacement is never made silently.
Compatibility limits
Catalog specifications and known constraints are buyer guidance. Confirming fit with an existing system requires the full parts list plus dimensions, power, interfaces, and software requirements.
Delivery and returns
Pickup or local delivery is agreed after availability is confirmed. Returns and cancellations depend on order state and the applicable terms.
Pricing basis
The page labels whether pricing uses recent market evidence, a planning estimate, or a written quote. A component price does not imply whole-system assembly, software setup, or completed-system testing.