L
localai.computer
ModelsGPUsSystemsBuildsOpenClawMethodology

Resources

  • Methodology
  • Submit Benchmark
  • About

Browse

  • AI Models
  • GPUs
  • PC Builds
  • AI News

Guides

  • OpenClaw Guide
  • How-To Guides

Legal

  • Privacy
  • Terms
  • Contact

© 2026 localai.computer. Hardware recommendations for running AI models locally.

ℹ️We earn from qualifying purchases through affiliate links at no extra cost to you. This supports our free content and research.

  1. Home
  2. Models
  3. NVIDIA Personaplex 7B V1

NVIDIA Personaplex 7B V1

14GB VRAM (FP16)
7B parametersReleased 2025-018,192 token context

Minimum VRAM

14GB

FP16 (full model) • Q4 option ≈ 4GB

Best Performance

AMD Instinct MI300X

~290 tok/s • FP16

Most Affordable

RX 7600 XT

FP16 • ~17 tok/s • From $329

Decision actions

AMD Instinct MI300X buy options →NVIDIA H200 SXM 141GB buy options →NVIDIA H100 SXM5 80GB buy options →Best GPU guides →Prebuilt systems →Local AI builds →

VRAM requirements at a glance

Q4 minimum
4GB
Q4_K_M
4GB
Q5_K_M
6GB
Q8 minimum
7GB
FP16 minimum
14GB

Quick answer: NVIDIA Personaplex 7B V1 needs roughly 4GB VRAM for Q4_K_M and 6GB for Q5_K_M. Use Q8 (7GB) or FP16 (14GB) for higher quality output.

Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.


Compatible GPUs

Filter by quantization, price, and VRAM to compare performance estimates.

ℹ️Speeds are estimates based on hardware specs. Actual performance depends on software configuration. Learn more

Showing FP16 compatibility. Switch tabs to explore other quantizations.

GPUSpeedVRAM RequirementTypical price
AMD Instinct MI300XEstimated
AMD
~290 tok/s
FP16
14GB VRAM used192GB total on card
$15,000View GPU →
NVIDIA H200 SXM 141GBEstimated
NVIDIA
~262 tok/s
FP16
14GB VRAM used141GB total on card
$35,000View GPU →
NVIDIA H100 SXM5 80GBEstimated
NVIDIA
~188 tok/s
FP16
14GB VRAM used80GB total on card
$30,000View GPU →
AMD Instinct MI250XEstimated
AMD
~181 tok/s
FP16
14GB VRAM used128GB total on card
$11,000View GPU →
NVIDIA H100 PCIe 80GBEstimated
NVIDIA
~119 tok/s
FP16
14GB VRAM used80GB total on card
$25,000View GPU →
RTX 5090Estimated
NVIDIA
~114 tok/s
FP16
14GB VRAM used32GB total on card
$1,999View GPU →
NVIDIA A100 80GB SXM4Estimated
NVIDIA
~111 tok/s
FP16
14GB VRAM used80GB total on card
$11,000View GPU →
AMD Instinct MI210Estimated
AMD
~90 tok/s
FP16
14GB VRAM used64GB total on card
$6,000View GPU →
NVIDIA A100 40GB PCIeEstimated
NVIDIA
~86 tok/s
FP16
14GB VRAM used40GB total on card
$9,000View GPU →
RTX 4090Estimated
NVIDIA
~68 tok/s
FP16
14GB VRAM used24GB total on card
$1,599View GPU →
NVIDIA RTX 6000 AdaEstimated
NVIDIA
~68 tok/s
FP16
14GB VRAM used48GB total on card
$6,999View GPU →
NVIDIA L40Estimated
NVIDIA
~63 tok/s
FP16
14GB VRAM used48GB total on card
$7,999View GPU →
NVIDIA L40SEstimated
NVIDIA
~63 tok/s
FP16
14GB VRAM used48GB total on card
$10,000View GPU →
RTX 5080Estimated
NVIDIA
~60 tok/s
FP16
14GB VRAM used16GB total on card
$1,199View GPU →
RTX 3090Estimated
NVIDIA
~59 tok/s
FP16
14GB VRAM used24GB total on card
$1,499View GPU →
RX 7900 XTXEstimated
AMD
~55 tok/s
FP16
14GB VRAM used24GB total on card
$999View GPU →
AMD Radeon Pro W7900Estimated
AMD
~55 tok/s
FP16
14GB VRAM used48GB total on card
$3,999View GPU →
RTX 5070 TiEstimated
NVIDIA
~55 tok/s
FP16
14GB VRAM used16GB total on card
$799View GPU →
NVIDIA A6000Estimated
NVIDIA
~50 tok/s
FP16
14GB VRAM used48GB total on card
$4,699View GPU →
RTX 4080 SuperEstimated
NVIDIA
~48 tok/s
FP16
14GB VRAM used16GB total on card
$999View GPU →
RTX 3080Estimated
NVIDIA
~48 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used10GB total on card
$699View GPU →
NVIDIA A5000Estimated
NVIDIA
~48 tok/s
FP16
14GB VRAM used24GB total on card
$2,399View GPU →
RTX 4080Estimated
NVIDIA
~47 tok/s
FP16
14GB VRAM used16GB total on card
$1,199View GPU →
RX 7900 XTEstimated
AMD
~46 tok/s
FP16
14GB VRAM used20GB total on card
$899View GPU →
RTX 4070 Ti SuperEstimated
NVIDIA
~43 tok/s
FP16
14GB VRAM used16GB total on card
$799View GPU →
RTX 5070Estimated
NVIDIA
~41 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$599View GPU →
Apple M2 UltraEstimated
Apple
~41 tok/s
FP16
14GB VRAM used192GB total on card
$5,999View GPU →
RX 9070 XTEstimated
AMD
~37 tok/s
FP16
14GB VRAM used16GB total on card
$599View GPU →
RX 7800 XTEstimated
AMD
~36 tok/s
FP16
14GB VRAM used16GB total on card
$499View GPU →
RX 7900 GREEstimated
AMD
~35 tok/s
FP16
14GB VRAM used16GB total on card
$649View GPU →
AMD Radeon Pro W7800Estimated
AMD
~34 tok/s
FP16
14GB VRAM used32GB total on card
$2,499View GPU →
RTX 4070 TiEstimated
NVIDIA
~34 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$799View GPU →
RTX 4070 SuperEstimated
NVIDIA
~33 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$599View GPU →
RX 9070Estimated
AMD
~33 tok/s
FP16
14GB VRAM used16GB total on card
$499View GPU →
Intel Arc A770 16GBEstimated
Intel
~33 tok/s
FP16
14GB VRAM used16GB total on card
$349View GPU →
RTX 4070Estimated
NVIDIA
~32 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$599View GPU →
RX 6900 XTEstimated
AMD
~31 tok/s
FP16
14GB VRAM used16GB total on card
$999View GPU →
RX 6800 XTEstimated
AMD
~31 tok/s
FP16
14GB VRAM used16GB total on card
$649View GPU →
Intel Arc A750Tight VRAM
Intel
~30 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used8GB total on card
$289View GPU →
NVIDIA A4000Estimated
NVIDIA
~29 tok/s
FP16
14GB VRAM used16GB total on card
$999View GPU →
RTX 3070Tight VRAM
NVIDIA
~29 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used8GB total on card
$499View GPU →
Intel Arc B580Estimated
Intel
~29 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$249View GPU →
Apple M4 MaxEstimated
Apple
~28 tok/s
FP16
14GB VRAM used128GB total on card
$3,999View GPU →
RX 7700 XTEstimated
AMD
~26 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$449View GPU →
Intel Arc B570Estimated
Intel
~24 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used10GB total on card
$219View GPU →
Intel Arc Pro A60Estimated
Intel
~23 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$599View GPU →
NVIDIA L4Estimated
NVIDIA
~23 tok/s
FP16
14GB VRAM used24GB total on card
$5,000View GPU →
RTX 3060 12GBEstimated
NVIDIA
~22 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used12GB total on card
$329View GPU →
Apple M3 MaxEstimated
Apple
~20 tok/s
FP16
14GB VRAM used128GB total on card
$3,999View GPU →
Apple M2 MaxEstimated
Apple
~20 tok/s
FP16
14GB VRAM used96GB total on card
$3,199View GPU →
RTX 4060 Ti 16GBEstimated
NVIDIA
~19 tok/s
FP16
14GB VRAM used16GB total on card
$499View GPU →
RTX 4060 Ti 8GBTight VRAM
NVIDIA
~19 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used8GB total on card
$399View GPU →
RTX 4060Tight VRAM
NVIDIA
~17 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used8GB total on card
$299View GPU →
RX 7600 XTEstimated
AMD
~17 tok/s
FP16
14GB VRAM used16GB total on card
$329View GPU →
Intel Arc Pro A40Estimated
Intel
~17 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used6GB total on card
$399View GPU →
RX 7600Tight VRAM
AMD
~17 tok/s
FP16⚠ Insufficient VRAM
14GB VRAM used8GB total on card
$269View GPU →
Apple M4 ProEstimated
Apple
~14 tok/s
FP16
14GB VRAM used64GB total on card
$1,999View GPU →
AMD Ryzen AI Max+ 395Estimated
AMD
~14 tok/s
FP16
14GB VRAM used128GB total on card
EnterpriseView GPU →
AMD Ryzen AI Max 385Estimated
AMD
~14 tok/s
FP16
14GB VRAM used128GB total on card
EnterpriseView GPU →
AMD Ryzen AI Max Pro 385Estimated
AMD
~14 tok/s
FP16
14GB VRAM used128GB total on card
EnterpriseView GPU →
Apple M2 ProEstimated
Apple
~10 tok/s
FP16
14GB VRAM used32GB total on card
$1,999View GPU →
Apple M3 ProEstimated
Apple
~8 tok/s
FP16
14GB VRAM used36GB total on card
$1,999View GPU →
Don't see your GPU? View all compatible hardware →
Best GPU Options for NVIDIA Personaplex 7B V1

NVIDIA Personaplex 7B V1 7B parametre içerir ve 4GB VRAM gerektirir - choose the best GPU for your needs

RecommendedBest Value
AMD Instinct MI300X
VRAM192GB
Price$150
View on Amazon

For Better Performance

Run NVIDIA Personaplex 7B V1 faster with AMD Instinct MI300X. For just $150 more, significantly boost your tokens/sec performance.

Browse All GPUs
Faster inference speed
Run larger models

Detailed Specifications

Hardware requirements and model sizes at a glance.

Technical details

Parameters
7,000,000,000 (7B)
Architecture
Transformer
Developer
—
Released
January 2025
Context window
8,192 tokens

Quantization support

Q4
4GB VRAM required • 4GB download
Q4_K_M
4GB VRAM required • 4GB download
Q5_K_M
6GB VRAM required • 7GB download
Q8
7GB VRAM required • 7GB download
FP16
14GB VRAM required • 14GB download

Hardware Requirements

ComponentMinimumRecommendedOptimal
VRAM4GB (Q4)7GB (Q8)14GB (FP16)
RAM16GB32GB64GB
Disk10GB20GB-
Model size4GB (Q4)7GB (Q8)14GB (FP16)
CPUModern CPU (Ryzen 5/Intel i5 or better)Modern CPU (Ryzen 5/Intel i5 or better)Modern CPU (Ryzen 5/Intel i5 or better)

Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data


Quantization requirement shortcuts
Built for high-intent queries like "NVIDIA Personaplex 7B V1 q4 vram requirements".
Q4 VRAM usageQ4_K_M VRAM usageQ5_K_M VRAM usageQ8 VRAM usageFP16 VRAM usage
Model speed shortcuts
Direct answers for "NVIDIA Personaplex 7B V1 speed on [GPU]" searches.
NVIDIA Personaplex 7B V1 speed on Apple M4 Max
Q4 • ~73 tok/s
NVIDIA Personaplex 7B V1 speed on RTX 4090
Q4 • ~180 tok/s
NVIDIA Personaplex 7B V1 speed on RTX 5090
Q4 • ~300 tok/s
NVIDIA Personaplex 7B V1 speed on RTX 5080
Q4 • ~158 tok/s
NVIDIA Personaplex 7B V1 speed on NVIDIA L4
Q4 • ~61 tok/s
Best GPU buying guides →Compare prebuilt systems →Local AI build recipes →

Frequently Asked Questions

Common questions about running NVIDIA Personaplex 7B V1 locally

What should I know before running NVIDIA Personaplex 7B V1?

This model delivers strong local performance when paired with modern GPUs. Use the hardware guidance below to choose the right quantization tier for your build.

How do I deploy this model locally?

Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).

Which quantization should I choose?

Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.

What is the difference between Q4, Q4_K_M, Q5_K_M, and Q8 quantization for NVIDIA Personaplex 7B V1?

Q4_K_M and Q5_K_M are GGUF quantization formats that balance quality and VRAM usage. Q4_K_M uses about 4GB VRAM. Q5_K_M uses about 6GB VRAM and keeps more accuracy. Q8 (~7GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for NVIDIA Personaplex 7B V1.

Where can I download NVIDIA Personaplex 7B V1?

Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.


Related models

Xgen Universe Capybara— params
Nineninesix Kani Tts 2 En— params
Unsloth Qwen3 5 397B A17b Gguf397B params

Compare models

See how NVIDIA Personaplex 7B V1 compares to other popular models.

All comparisons →NVIDIA Personaplex 7B V1 vs others