Performance Comparison of the Latest Locally-Running LLMs

– A 2025 Model & Machine Spec Guide for People Who Seriously Want “AI on My Own PC” –

1. What This Article Aims to Tell You

Since the start of 2025, demand for

“Running an LLM locally on my own PC instead of in the cloud”

has increased sharply.

The reasons are straightforward:

• You want to keep monthly subscription costs down
• You don’t want to send confidential data to the cloud
• You want to use it offline
• You want to do custom training or plugin-style integrations

These are all very real needs at the front lines.

On the other hand, it’s genuinely hard to tell:

• Which models are the “real contenders” right now
• How far you can go with your machine specs
• Whether it’s usable without a GPU

So in this article, based on information as of the end of 2025, we’ll:

1. Compare the performance and characteristics of popular, up-to-date LLMs used locally
2. Clarify what level of model runs comfortably on what kind of machine
3. Show which models fit which use cases (chat / programming / math & reasoning)

in as clear and practical a way as possible.

2. Big Picture: “Modern LLMs” You Can Run Locally

First, let’s outline the main models that frequently come up in the local-usage context in 2025.

2-1. Model Lineup (7–10B Is the Current Sweet Spot)

Representative open-source models considered suitable for local use include:

• Llama 3.1 8B / 70B (Meta)

  • Available in 8B / 70B / 405B.
  • The 8B model is lightweight yet high-performance, with multilingual support and 128K context.

• Qwen2.5 7B / 14B / 32B (Alibaba / Qwen)

  • In the 2.5 generation, knowledge and reasoning performance improved substantially.
  • The 7B model hits MMLU 74.2, making it a top-tier performer in its class.

• Gemma 2 2B / 9B / 27B (Google)

  • A Google model explicitly designed with local execution in mind.
  • The 9B version strikes a good balance between performance and size, with reports of running on 4-bit quantization with around 7–8GB of VRAM.

• Phi-4 mini / mini-flash / mini-reasoning (Microsoft, ~3.8B)

  • Despite having only 3.8B parameters, it outperforms models more than twice its size in math and reasoning tasks.
  • Optimized for mobile and edge devices, with support for 64K–128K context.

• Mistral / Mixtral / Nemo family (Mistral AI / NVIDIA)

  • Includes models like Mixtral 8x7B and Mistral Nemo 12B, which use Mixture-of-Experts (MoE) architectures to achieve high performance efficiently.

These models can be run locally via tools like Ollama / LM Studio / local-gemma / llama.cpp, and as of 2025 the mainstream pattern has become:

“Run a 7–10B model on a single GPU.”

3. Model-by-Model: Rough Comparison of Performance and “Personality”

There are endless detailed benchmarks, but here we’ll focus on practical, “how they feel” differences.

3-1. Llama 3.1 8B: Solid, Balanced “Center of the Field”

Characteristics

• Among the 8B / 70B / 405B line, the practical choice for local use is the 8B model.
• Offers 128K token context, multilingual support, and a solid balance of reasoning, coding, and math performance.

Performance (Intuitively)

• Compared with other 7–9B open models, it serves as a kind of reference model for overall chat quality, Japanese support, and stability.
• It doesn’t reach GPT-4o mini or Gemini 1.5 Flash level, but as an “8B model you can run locally” it’s considered quite competitive.

Local Execution Guidelines

• Full precision (FP16):
  • A 12GB-class GPU (RTX 3060–4070, etc.) is a good match.
• With 4-bit quantization:
  • Runs realistically on 8GB-class GPUs, though with some loss in speed and accuracy.

Good Use Cases

• General-purpose chatbots
• Multilingual text generation including Japanese
• Light programming assistance
• Base model for internal QA bots

3-2. Qwen2.5 7B / 14B: “Practical” Model with Strong Multilingual & Coding Skills

Characteristics

• From Qwen2 to Qwen2.5, data increased from 7T to up to 18T tokens, significantly boosting knowledge and reasoning.
• The 7B model hits MMLU 74.2, while the 72B model scores 86.1 — very high for its size.
• Has abundant task-specific variants, like Coder and Math models.

Performance

• Strong in general chat, but especially in:
  • Coding (Qwen2.5-Coder)
  • Math & reasoning (Qwen2.5-Math)
    where it tends to stand out.

Local Execution Guidelines

• 7B models:
  • Plenty of reports of smooth use on 8–12GB VRAM GPUs with 4-bit quantization.
• 14B models:
  • Realistically needs 16–24GB VRAM (RTX 4080 / 4090 class).

Good Use Cases

• Programming assistance / code generation / refactoring
• Math and algorithmic problem solving (strong results on GSM8K, MATH, etc.)
• Mixed-language environments (Chinese + English + Japanese, broader Asian multilingual contexts)
• Technical blog / documentation generation

3-3. Gemma 2 9B: A Google Model Designed “With Local in Mind”

Characteristics

• Available in 2B / 9B / 27B sizes.
• Officially positioned by Google as “easy to run locally,” with a special local-gemma tool provided on Hugging Face.
• The 9B model has:
  • ~9B parameters
  • 8,192-token context
  • Modern architecture elements like GQA and RoPE.

VRAM / Storage Requirements

• Raw 9B model:
  • ~40GB disk, ~40GB VRAM (or ~20GB in BF16), 8GB+ RAM recommended.
• Example 4-bit quantized local-gemma presets:
  • “Memory” preset: 9B uses ~7.3GB VRAM
  • “Memory Extreme”: reported to run with as little as ~3.7GB VRAM via CPU offloading.

Performance

• Evaluated as a “jack-of-all-trades” at a similar class to Llama 3.1 8B,
  especially with high marks for natural English generation and logical text structure.

Good Use Cases

• General chat and text generation
• International teams that mainly use English with Japanese as a secondary language
• Users who want local inference now and potential future integration with Google tools

3-4. Phi-4 mini / mini-flash / mini-reasoning: A “Tiny Monster” at 3.8B

Characteristics

• With just 3.8B parameters, it:
  • Beats models more than twice its size on math benchmarks (GPQA, Math500, etc.)
  • Is touted by Microsoft as comparable to OpenAI’s o1-mini in some tasks.
• Comes in variants:
  • mini: general-purpose
  • mini-flash: low-latency / high-throughput
  • mini-reasoning: math & logical reasoning focused
• Supports 64K–128K context and is designed to run on mobile / NPUs.

Local Execution Guidelines

• At 3.8B, it’s small enough that:
  • CPU-only machines without a GPU
  • Laptops with 8–16GB RAM
    can run it at very usable speeds.

Performance

• Exceptionally strong on reasoning tasks for its size — great if you want a “light but sharp” model.
• For natural chat style, Llama 3.1 and Gemma 2 can still win in some scenarios,
  but for math, logic, and algorithmic questions, it’s extremely capable.

Good Use Cases

• Local LLM starter setup on a GPU-less PC
• “Tutor” for math and programming learning
• Lightweight chatbots / inference on edge devices

3-5. Mistral / Mixtral / Nemo Family: MoE-Based “Heavy but Powerful” Models

Characteristics

• Models like Mixtral 8x7B use Mixture-of-Experts architectures:
  “Internally large, but only a subset is active per token,” increasing efficiency.
• NVIDIA’s Mistral Nemo 12B is heavily optimized for GPUs, and delivers high throughput on RTX 4090-class hardware.

Local Execution Guidelines

• For truly comfortable use, you’re realistically looking at:
  • RTX 4090 (24GB VRAM) + 64GB RAM or more
  • Or A100 / H100-class GPUs

Good Use Cases

• Running an on-prem LLM server to provide AI services to multiple users
• Organizations or labs that want high-performance models without relying on the cloud
• Heavy workloads like code completion and analysis over large codebases

4. Which Model Classes Are Realistic for Your Machine Specs?

Now let’s walk through typical PC specs and roughly what model sizes you can expect to run comfortably.

4-1. Laptop with No Dedicated GPU (Around 16GB RAM)

Example Specs

• CPU: Laptop Core i5 / Ryzen 5 class
• GPU: Integrated (no discrete GPU)
• Memory: 16GB
• Storage: 512GB SSD

Realistic Model Sizes

• Up to ~3–4B models are your main targets:
  • e.g., Phi-4-mini / mini-flash / mini-reasoning, smaller Qwen2.5 variants around 1–3B.
• 7B models can run in 4-bit quantization on CPU only, but:
  • Responses will be quite slow
  • Long workloads will strain battery and thermals

Good Use Cases

• Light chat and text generation
• Study use for math and algorithms (Phi-4 mini family)
• “First try” phase of local LLMs on mobile or laptop hardware

4-2. Mid-Range GPU Desktop (RTX 3060–4070, 8–12GB VRAM)

Example Specs

• GPU: RTX 3060 / 4060 / 4070 (8–12GB VRAM)
• Memory: 32GB RAM
• Storage: 1TB SSD

In Gemma 2’s local environment docs,
recommendations like “RTX 3060 (12GB VRAM), 32GB RAM, 500GB–1TB SSD” are given as a minimum setup.

Realistic Model Sizes

• 7–9B models with 4-bit quantization:
  • Llama 3.1 8B, Qwen2.5 7B, Gemma 2 9B (using local-gemma Memory preset), etc.
• 3–4B models will feel “overkill-level” smooth.

Good Use Cases

• Daily chat + document creation for work
• Programming assistance and code review
• Internal QA bots / small-scale knowledge search

This spec range is becoming the de facto standard setup for people who “seriously use local LLMs at home.”

4-3. High-End GPU Machines (RTX 4080 / 4090, 16–24GB VRAM)

Example Specs

• GPU: RTX 4080 / 4090 (16–24GB VRAM)
• Memory: 64GB RAM or more
• Storage: 1–2TB SSD

With RTX 4090 + Ollama setups, benchmarks show
that multiple models (Llama / Qwen / Gemma / DeepSeek, etc.) can be hosted at high speed.

Realistic Model Sizes

• 14B–27B models in 4-bit quantization
• Multiple models running concurrently (e.g., one for chat and one for coding)

Good Use Cases

• On-prem “AI server” for a small team
• Heavy coding models (Qwen2.5-Coder 14B/32B, etc.)
• R&D, benchmarking, and custom fine-tuning

4-4. Workstations / Servers (A100 / H100, Multi-GPU)

Example Specs

• GPU: A100 40GB / H100 80GB × 1 or multiple
• Memory: 128GB+
• Storage: 2TB+

Gemma 2’s system requirements also cite A100 / H100-level setups as optimal examples.

Realistic Model Sizes

• Full-precision inference on 70B-class models (e.g., Llama 3.1 70B)
• High-throughput, multi-user serving

Good Use Cases

• On-prem AI platforms for enterprises and research institutions
• Large-scale knowledge search and internal Copilot-style services
• Fine-tuning (including RL-based methods) and advanced research

For individual users this is generally overkill,
but for organizations that want to keep cloud-grade LLM capabilities fully on-prem,
it’s becoming a realistic option.

5. How to Read Performance Benchmarks Without Getting Overwhelmed

LLM performance reports are full of acronyms:

• MMLU
• GSM8K
• HumanEval
• GPQA
• MT-Bench

It’s honestly a lot.

Here’s a simplified way to think about them:

5-1. Rough Categories of Benchmarks

• MMLU: Broad academic and general knowledge tasks
  → A rough measure of “breadth of knowledge / general education.”
• GSM8K / MATH: Math word problems
  → Measures calculation, logic, and numerical reasoning.
• HumanEval / MBPP: Programming tasks
  → Evaluates code generation and algorithmic understanding.
• MT-Bench: Human-evaluated dialogue / reasoning / instruction following.

Qwen2.5 and Phi-4 mini are known to be strong on math-related benchmarks like GSM8K and MATH,
and are thus “small but quick-thinking” models.

Llama 3.1 8B and Gemma 2 9B tend to score well in overall metrics like MMLU and MT-Bench,
making them more “well-rounded generalists” that perform consistently across tasks.

5-2. Factors That Drive Real-World “Usability”

Beyond benchmark scores, for local usage these matter a lot:

• Response speed (tokens per second)
• Naturalness of Japanese (or your main language)
• Obedience to instructions (does it go off on tangents?)
• Context length (how much material you can feed at once)
• Model stability (how prone it is to “going off the rails”)

For example, Phi-4 mini-flash uses a new hybrid architecture that reportedly gives:

• 10× throughput
• 2–3× lower latency

compared to previous models, making it very attractive in terms of practical responsiveness.

6. By Use Case: Which Model + Spec Combo Works Best?

Finally, here are a few common needs and example combinations of model + machine spec that match them.

6-1. Main Use: Daily Chat + Blogging / Document Writing

Candidate Models

• Llama 3.1 8B
• Gemma 2 9B
• (For lighter setups) Phi-4 mini family

Recommended Specs

• No GPU:
  → Use Phi-4 mini as your main model.
• RTX 3060–4070 (8–12GB VRAM):
  → Run Llama 3.1 8B / Gemma 2 9B in 4-bit quantization comfortably.

Key Points

• For natural Japanese writing and good structure,
  8–9B-class models provide a strong sense of reliability.
• If you handle large volumes of text, choose at least an 8B model as your main workhorse.

6-2. Heavy Use of Programming Assistance / Code Generation

Candidate Models

• Qwen2.5-Coder 7B / 14B
• Llama 3.1 8B (general + code)

Recommended Specs

• For 7B:
  • RTX 3060–4070 (8–12GB VRAM) + 32GB RAM
• For 14B:
  • RTX 4080 / 4090 (16–24GB VRAM) + 64GB RAM

Key Points

• For “Copilot-like” experiences in your IDE with a local LLM,
  7B models are already quite practical.
• If you also want it to generate tests, refactor code, and deeply understand complex repos,
  14B-class models and high-end GPUs give you more headroom.

6-3. Math, Reasoning, and Research-Heavy Use

Candidate Models

• Phi-4-mini-reasoning / mini-flash
• Qwen2.5-Math 7B

Recommended Specs

• GPU-less to mid-range GPUs are sufficient (3.8B–7B class).

Key Points

• In math and logic tasks, “smartness per parameter” matters more than sheer size,
  and Phi-4 mini-level models already perform impressively.
• Start with lightweight models and expand to Qwen2.5-Math if you need more power.

7. Summary: Simple Guidelines for Choosing a Local LLM

This was a lot of information, so let’s close with a simple decision flow for choosing models.

1. First, let your machine specs define the upper bound

   • No GPU → Up to ~4B (Phi-4 mini, etc.)
   • 8–12GB VRAM → 7–9B (Llama 3.1 8B, Qwen2.5 7B, Gemma 2 9B in 4-bit)
   • 16–24GB VRAM → 14–27B also becomes realistic
   • 40GB+ VRAM → 70B-class models become feasible

2. Next, pick a model family based on your main use case

   • General-purpose → Llama 3.1 / Gemma 2
   • Coding-focused → Qwen2.5-Coder
   • Math & reasoning → Phi-4 mini / Qwen2.5-Math

3. Finally, actually try them and choose by “feel”

   • Response speed
   • Language style (especially in Japanese)
   • How well they follow instructions

These are “fit” factors you can’t fully see from numbers,
so the most reliable method is to test 2–3 models side by side with tools like Ollama or local-gemma.

Compared to giant cloud models, local LLMs still have more constraints, but:

• You can control costs more easily
• You don’t need to send sensitive data off your machine
• You can customize them to your own preferences

All of which mean they’re likely to become even more important in real-world, practical environments going forward.