[Request for Validation] Gemma 4 E2B at average 2 GB RAM and 35+ t/s on a 16 GB Laptop (CPU Only)
Posted by Apprehensive-Scale90@reddit | LocalLLaMA | View on Reddit | 10 comments
Gemma 4 is Google DeepMinds latest open model family released on April 2 2026.
The E2B variant is an edge focused model with 2.3B effective parameters that supports text image and audio while delivering strong reasoning in a very efficient package.
I have been digging into the default RAM bloat on the new Gemma 4 E2B on my HP Pavilion with an i7 1165G7 and 16 GB RAM (no discrete GPU) it was using 7.4 GB and running at only 12 to 15 tokens per second.
By applying a lean config I dropped the footprint to average 2 GB RAM with much snappier responses. I want to know if others can replicate this on similar mobile hardware.
The Theory
The real culprit is not the model weights but the default 128K context pre allocating a massive KV cache. On CPU systems this reserves several gigabytes of ghost RAM before you even start chatting and causes heavy memory management overhead.
Lean Config (Ollama Modelfile)
Create a Modelfile with these overrides:
text
FROM gemma4:e2b-it-q4_K_M
# Cap context to reclaim roughly 4 GB RAM
PARAMETER num_ctx 2048
# Lock to physical cores to avoid thread thrashing
PARAMETER num_thread 4
# Force direct responses and bypass internal reasoning loop
SYSTEM "You are a concise assistant. Respond directly and immediately. No internal monologue or step by step reasoning unless explicitly asked."
Optional extra (PowerShell on Windows):
text
$env:OLLAMA_KV_CACHE_TYPE="q4_0"
Benchmarks on i7 1165G7 / 16 GB RAM
I tested four scenarios to check the speed versus quality tradeoff:
| Task Type | Prompt Eval (t/s) | Generation (t/s) | Result |
|---|---|---|---|
| Simple Retrieval | 99.35 | 16.88 | Pass |
| Conceptual (Thermodynamics) | 120.20 | 15.68 | Pass |
| Logic Puzzle (Theory of Mind) | 252.89 | 35.08 | Fail |
| Agentic Data Extraction | 141.87 | 16.65 | Pass |
Key Findings
- Capping context at 2048 tokens delivers a huge prompt eval spike and near instant time to first token.
- Suppressing the thinking mode gives excellent speed but hurts performance on trickier logic questions (for example it answered 3 instead of 1 on a classic Sally Anne false belief test).
- Structured extraction tasks remained rock solid.
Request for Validation
If you have a 16 GB laptop (especially Intel i5 or i7 mobile CPUs), please try this config and reply with your results:
- Did model RAM usage drop to average 2 GB or below?
- What generation speed (tokens per second) did you see?
- Any noticeable drop in reasoning quality on logic or agentic tasks?
If this validates across hardware it could let us run a capable edge model as a lightweight background planner with almost zero system impact.
Looking forward to your tests and suggestions for further tweaks.
#LocalLLM #Gemma4 #Ollama #EdgeAI #LLMOptimization
emmettvance@reddit
This is quite a solid optimization. The kv cache bloat on 128k context is the real problem for laptop ram. Have you tested intermediate context sizes like 4k or 8k to find the sweet spot where reasoning tasks dont fallout but you still get significant ram savings? The jump from 2048 to 128k seems a bit extreme to me... i guess there might be a middle ground where you keep soem reasoning capability without the full 7.4gb footprint
Apprehensive-Scale90@reddit (OP)
Thanks for feedback.
not yet, its good idea to try with different size to find sweet spot. will try it.
Apprehensive-Scale90@reddit (OP)
I have tried with 8K context window running directly from llama.cpp
result : 13.5 token/sec
Was testing with Live market data for analysis
NBIS (Price: $124.63, +6.16%)
Input fed : RSI 77.8, MACD 4.83/Signal 3.21/Hist 1.62, BB %B 0.84, ATR 8.92, SMA50 108.45, SMA200 95.20, EMA20 116.30, VWAP 122.50, Volume 451
Output:
Compared this with my grok pipeline, the result is sold except grok fetch live sentiments.
For offline or overnight task - running Gamm4 locally with 8K is solid, depending on token size the context window can be optimized (4K, 6K, 8K), receive such high grade LLM reasoning in local environment is gold mine.
MelodicRecognition7@reddit
lol
Apprehensive-Scale90@reddit (OP)
Thanks, This are the good practice have to follow it.
Apprehensive-Scale90@reddit (OP)
>>> What are the three primary colors of light, and what is their hexadecimal code?
Red: #FF0000, Green: #00FF00, Blue: #0000FF
total duration: 2.5671461s
load duration: 343.4315ms
prompt eval count: 58 token(s)
prompt eval duration: 583.8117ms
prompt eval rate: 99.35 tokens/s
eval count: 27 token(s)
eval duration: 1.5999832s
eval rate: 16.88 tokens/s
>>> Explain the relationship between entropy, the second law of thermodynamics, and the concept of time's arrow in exact
... ly three sentences
The Second Law of Thermodynamics states that the total entropy of an isolated system always increases over time.
This increase in entropy dictates the direction of spontaneous processes, meaning systems naturally evolve toward
states of greater disorder. This relentless increase in entropy defines the directionality of time, establishing
what is known as the thermodynamic arrow of time.
total duration: 5.44275s
load duration: 321.0074ms
prompt eval count: 118 token(s)
prompt eval duration: 981.6627ms
prompt eval rate: 120.20 tokens/s
eval count: 64 token(s)
eval duration: 4.0820126s
eval rate: 15.68 tokens/s
>>> Sally has 3 brothers. Each of her brothers has 2 sisters. How many sisters does Sally have?
3
total duration: 1.3708917s
load duration: 463.959ms
prompt eval count: 213 token(s)
prompt eval duration: 842.2689ms
prompt eval rate: 252.89 tokens/s
eval count: 2 token(s)
eval duration: 57.0096ms
eval rate: 35.08 tokens/s
>>> Extract the date, vendor, and total amount from this log entry: 'ERROR 2026-04-07 10:15:02 - Payment failed for user
... _99 at Iselin_Pizza_Hub - Status: 402 - Amount: $42.50
Date: 2026-04-07, Vendor: Iselin_Pizza_Hub, Amount: $42.50
total duration: 4.3995915s
load duration: 399.1375ms
prompt eval count: 291 token(s)
prompt eval duration: 2.0511208s
prompt eval rate: 141.87 tokens/s
eval count: 32 token(s)
eval duration: 1.9215151s
eval rate: 16.65 tokens/s
Miserable-Dare5090@reddit
Yeah dude, no one is guessing you made up your use of ollama and your grand discovery that cache takes up space. It’s like saying a car runs on a single gallon of gas vs a full tank, sure, how far do you get? It’s also telling you used ollama, a llama.cpp wrapper that almost no one with enough time using local models will touch bc its features are not worth the slow down in function, which can be like a 25% tax to your speed.
Even LMStudio and Lemonade have features that make the overhead worth it, but ollama is just an often hated piece of software.
Apprehensive-Scale90@reddit (OP)
Thanks for you insight and valuable feedback.
it is just Day1 for me to run Gamma 4 on laptop, have to try with native llama.cpp.
Problem I'm trying to tackle is Running LLM with reasonable reasoning under hard constraint <3-5 sec response and <6-8 GB RAM on CPU
Even if there is any other tool/model available I'll winning to try it.
MelodicRecognition7@reddit
see above
Apprehensive-Scale90@reddit (OP)
>>> What are the three primary colors of light, and what is their hexadecimal code?
Red: #FF0000, Green: #00FF00, Blue: #0000FF
total duration: 2.5671461s
load duration: 343.4315ms
prompt eval count: 58 token(s)
prompt eval duration: 583.8117ms
prompt eval rate: 99.35 tokens/s
eval count: 27 token(s)
eval duration: 1.5999832s
eval rate: 16.88 tokens/s
>>> Explain the relationship between entropy, the second law of thermodynamics, and the concept of time's arrow in exact
... ly three sentences
The Second Law of Thermodynamics states that the total entropy of an isolated system always increases over time.
This increase in entropy dictates the direction of spontaneous processes, meaning systems naturally evolve toward
states of greater disorder. This relentless increase in entropy defines the directionality of time, establishing
what is known as the thermodynamic arrow of time.
total duration: 5.44275s
load duration: 321.0074ms
prompt eval count: 118 token(s)
prompt eval duration: 981.6627ms
prompt eval rate: 120.20 tokens/s
eval count: 64 token(s)
eval duration: 4.0820126s
eval rate: 15.68 tokens/s
>>> Sally has 3 brothers. Each of her brothers has 2 sisters. How many sisters does Sally have?
3
total duration: 1.3708917s
load duration: 463.959ms
prompt eval count: 213 token(s)
prompt eval duration: 842.2689ms
prompt eval rate: 252.89 tokens/s
eval count: 2 token(s)
eval duration: 57.0096ms
eval rate: 35.08 tokens/s
>>> Extract the date, vendor, and total amount from this log entry: 'ERROR 2026-04-07 10:15:02 - Payment failed for user
... _99 at Iselin_Pizza_Hub - Status: 402 - Amount: $42.50
Date: 2026-04-07, Vendor: Iselin_Pizza_Hub, Amount: $42.50
total duration: 4.3995915s
load duration: 399.1375ms
prompt eval count: 291 token(s)
prompt eval duration: 2.0511208s
prompt eval rate: 141.87 tokens/s
eval count: 32 token(s)
eval duration: 1.9215151s
eval rate: 16.65 tokens/s